diff --git a/tools/benchmark_tool/openvino/tools/benchmark/utils/inputs_filling.py b/tools/benchmark_tool/openvino/tools/benchmark/utils/inputs_filling.py
index 272426abcb1..aef56bee1b4 100644
--- a/tools/benchmark_tool/openvino/tools/benchmark/utils/inputs_filling.py
+++ b/tools/benchmark_tool/openvino/tools/benchmark/utils/inputs_filling.py
@@ -260,7 +260,7 @@ def get_image_info_tensors(image_sizes, layer):
 
 def fill_tensors_with_random(layer):
     dtype = get_dtype(layer.element_type)
-    rand_min, rand_max = (0, 1) if dtype == np.bool else (np.iinfo(np.uint8).min, np.iinfo(np.uint8).max)
+    rand_min, rand_max = (0, 1) if dtype == bool else (np.iinfo(np.uint8).min, np.iinfo(np.uint8).max)
     # np.random.uniform excludes high: add 1 to have it generated
     if np.dtype(dtype).kind in ['i', 'u', 'b']:
         rand_max += 1
diff --git a/tools/benchmark_tool/openvino/tools/benchmark/utils/utils.py b/tools/benchmark_tool/openvino/tools/benchmark/utils/utils.py
index 589e8dd81d0..91e97f89aa1 100644
--- a/tools/benchmark_tool/openvino/tools/benchmark/utils/utils.py
+++ b/tools/benchmark_tool/openvino/tools/benchmark/utils/utils.py
@@ -528,7 +528,7 @@ def parse_scale_or_mean(parameter_string, input_info):
         if matches:
             for match in matches:
                 input_name, value = match
-                f_value = np.array(value.split(",")).astype(np.float)
+                f_value = np.array(value.split(",")).astype(np.float32)
                 if input_name != '':
                     return_value[input_name] = f_value
                 else:
diff --git a/tools/mo/openvino/tools/mo/analysis/boolean_input.py b/tools/mo/openvino/tools/mo/analysis/boolean_input.py
index 6c3e1536dec..562a3da27d0 100644
--- a/tools/mo/openvino/tools/mo/analysis/boolean_input.py
+++ b/tools/mo/openvino/tools/mo/analysis/boolean_input.py
@@ -10,7 +10,7 @@ from openvino.tools.mo.utils.model_analysis import AnalyzeAction
 class TrainingPhaseAnalysis(AnalyzeAction):
 
     def analyze(self, graph: Graph):
-        nodes = graph.get_op_nodes(op='Parameter', data_type=np.bool)
+        nodes = graph.get_op_nodes(op='Parameter', data_type=bool)
         names = ""
         params = ""
         if not nodes:
diff --git a/tools/mo/openvino/tools/mo/front/freeze_placeholder_value.py b/tools/mo/openvino/tools/mo/front/freeze_placeholder_value.py
index 7521bb378a3..77ed73d21f7 100644
--- a/tools/mo/openvino/tools/mo/front/freeze_placeholder_value.py
+++ b/tools/mo/openvino/tools/mo/front/freeze_placeholder_value.py
@@ -46,10 +46,10 @@ class FreezePlaceholderValue(FrontReplacementSubgraph):
                 data_type = SUPPORTED_DATA_TYPES[graph.graph['cmd_params'].data_type][0]
             string_value = graph.graph['freeze_placeholder'][name]
             try:
-                if data_type != np.bool:
+                if data_type != bool:
                     value = mo_array(string_value, dtype=data_type)
                 # TODO: investigate why boolean type is allowed only for TensorFlow
-                elif data_type == np.bool and graph.graph['fw'] == 'tf':
+                elif data_type == bool and graph.graph['fw'] == 'tf':
                     from openvino.tools.mo.front.tf.common import tf_data_type_cast
                     if isinstance(string_value, list):
                         casted_list = list()
diff --git a/tools/mo/openvino/tools/mo/front/kaldi/extractors/lstm_nonlinearity_ext.py b/tools/mo/openvino/tools/mo/front/kaldi/extractors/lstm_nonlinearity_ext.py
index b2a8c26f08b..e0505a07f92 100644
--- a/tools/mo/openvino/tools/mo/front/kaldi/extractors/lstm_nonlinearity_ext.py
+++ b/tools/mo/openvino/tools/mo/front/kaldi/extractors/lstm_nonlinearity_ext.py
@@ -22,7 +22,7 @@ class LSTMNonlinearityFrontExtractor(FrontExtractorOp):
         ifo_x_weights, ifo_x_weights_shape = read_binary_matrix(pb)
 
         try:
-            use_dropout = collect_until_token_and_read(pb, b'<UseDropout>', np.bool)
+            use_dropout = collect_until_token_and_read(pb, b'<UseDropout>', bool)
         except Error:
             # layer have not UseDropout attribute, so setup it to False
             use_dropout = False
diff --git a/tools/mo/openvino/tools/mo/front/kaldi/loader/utils.py b/tools/mo/openvino/tools/mo/front/kaldi/loader/utils.py
index df4df1791bb..ffd21a89f4f 100644
--- a/tools/mo/openvino/tools/mo/front/kaldi/loader/utils.py
+++ b/tools/mo/openvino/tools/mo/front/kaldi/loader/utils.py
@@ -258,7 +258,7 @@ def read_token_value(file_desc: io.BufferedReader, token: bytes = b'', value_typ
     getters = {
         np.uint32: read_binary_integer32_token,
         np.uint64: read_binary_integer64_token,
-        np.bool: read_binary_bool_token
+        bool: read_binary_bool_token
     }
     current_token = collect_until_whitespace(file_desc)
     if token != b'' and token != current_token:
@@ -314,7 +314,7 @@ def collect_until_token_and_read(file_desc: io.BufferedReader, token, value_type
     getters = {
         np.uint32: read_binary_integer32_token,
         np.uint64: read_binary_integer64_token,
-        np.bool: read_binary_bool_token,
+        bool: read_binary_bool_token,
         np.string_: read_string
     }
     collect_until_token(file_desc, token)
diff --git a/tools/mo/openvino/tools/mo/front/kaldi/restrictedattentioncomponent_replacer.py b/tools/mo/openvino/tools/mo/front/kaldi/restrictedattentioncomponent_replacer.py
index 608fea5e127..00edfb54741 100644
--- a/tools/mo/openvino/tools/mo/front/kaldi/restrictedattentioncomponent_replacer.py
+++ b/tools/mo/openvino/tools/mo/front/kaldi/restrictedattentioncomponent_replacer.py
@@ -177,7 +177,7 @@ class RestrictedAttentionComponentReplacer(FrontReplacementPattern):
 
         split_2_node.out_port(0).connect(einsum_1_node.in_port(1))
 
-        mul_node = create_op_with_const_inputs(graph, Mul, {1: mo_array(key_scale, dtype=np.float)},
+        mul_node = create_op_with_const_inputs(graph, Mul, {1: mo_array(key_scale, dtype=np.float32)},
                                                {'name': self.in_name + '/Mul'})
         reshape_helper_1_node.out_port(0).connect(mul_node.in_port(0))
 
diff --git a/tools/mo/openvino/tools/mo/front/onnx/LoopNormalize.py b/tools/mo/openvino/tools/mo/front/onnx/LoopNormalize.py
index 62025cca1c2..4cbde0b8658 100644
--- a/tools/mo/openvino/tools/mo/front/onnx/LoopNormalize.py
+++ b/tools/mo/openvino/tools/mo/front/onnx/LoopNormalize.py
@@ -39,7 +39,7 @@ class ONNXLoopNormalize(FrontReplacementSubgraph):
         # connect "execution condition" input if it is not connected with default value True
         if not loop_node.is_in_port_connected(1):
             loop_node.add_input_port(1, skip_if_exist=True)
-            Const(loop_node.graph, {'name': loop_name + '/execution_cond', 'value': mo_array(True, dtype=np.bool)}).\
+            Const(loop_node.graph, {'name': loop_name + '/execution_cond', 'value': mo_array(True, dtype=bool)}).\
                 create_node().out_port(0).connect(loop_node.in_port(1))
 
         # scan output need Unsqueeze over axis 0
diff --git a/tools/mo/openvino/tools/mo/front/onnx/extractors/utils.py b/tools/mo/openvino/tools/mo/front/onnx/extractors/utils.py
index 8381fe6dfde..de98422d7f1 100644
--- a/tools/mo/openvino/tools/mo/front/onnx/extractors/utils.py
+++ b/tools/mo/openvino/tools/mo/front/onnx/extractors/utils.py
@@ -56,7 +56,7 @@ def get_onnx_opset_version(node: Node):
 def get_onnx_datatype_as_numpy(value):
     datatype_to_numpy = {
         1: np.float32,
-        9: np.bool,
+        9: bool,
         11: np.double,
         10: np.float16,
         5: np.int16,
diff --git a/tools/mo/openvino/tools/mo/front/onnx/group_norm_ext.py b/tools/mo/openvino/tools/mo/front/onnx/group_norm_ext.py
index ca47807c5f7..92c7461ed0d 100644
--- a/tools/mo/openvino/tools/mo/front/onnx/group_norm_ext.py
+++ b/tools/mo/openvino/tools/mo/front/onnx/group_norm_ext.py
@@ -16,7 +16,7 @@ class ExperimentalDetectronGroupNorm(FrontExtractorOp):
     @classmethod
     def extract(cls, node):
         attrs = {
-            'eps': mo_array(onnx_attr(node, 'eps', 'f', default=1e-6), dtype=np.float),
+            'eps': mo_array(onnx_attr(node, 'eps', 'f', default=1e-6), dtype=np.float32),
             'num_groups': int64_array(onnx_attr(node, 'num_groups', 'i', default=1)),
         }
         GroupNorm.update_node_stat(node, attrs)
@@ -30,7 +30,7 @@ class GroupNormExtractor(FrontExtractorOp):
     @classmethod
     def extract(cls, node):
         attrs = {
-            'eps': mo_array(onnx_attr(node, 'eps', 'f', default=1e-6), dtype=np.float),
+            'eps': mo_array(onnx_attr(node, 'eps', 'f', default=1e-6), dtype=np.float32),
             'num_groups': int64_array(onnx_attr(node, 'num_groups', 'i', default=1)),
         }
         GroupNorm.update_node_stat(node, attrs)
diff --git a/tools/mo/openvino/tools/mo/front/tf/MapFNTransformation.py b/tools/mo/openvino/tools/mo/front/tf/MapFNTransformation.py
index 7d055600ddd..c688258b1fd 100644
--- a/tools/mo/openvino/tools/mo/front/tf/MapFNTransformation.py
+++ b/tools/mo/openvino/tools/mo/front/tf/MapFNTransformation.py
@@ -243,7 +243,7 @@ class MapFNOutputConcatenation(FrontReplacementSubgraph):
             if 'purpose' in record and record['purpose'] == 'execution_condition':
                 exec_cond_layer_id = record['internal_layer_id']
                 exec_cond_node = Loop.get_body_node_by_internal_id(loop_node, exec_cond_layer_id)
-                const_true = Const(body_graph, {'value': mo_array(True, dtype=np.bool)}).create_node()
+                const_true = Const(body_graph, {'value': mo_array(True, dtype=bool)}).create_node()
                 exec_cond_node.in_port(0).get_connection().set_source(const_true.out_port(0))
 
         # remove back edge
diff --git a/tools/mo/openvino/tools/mo/front/tf/WhileNormalize.py b/tools/mo/openvino/tools/mo/front/tf/WhileNormalize.py
index f280c1a8084..4bb0e0d1afa 100644
--- a/tools/mo/openvino/tools/mo/front/tf/WhileNormalize.py
+++ b/tools/mo/openvino/tools/mo/front/tf/WhileNormalize.py
@@ -34,7 +34,7 @@ class WhileNormalize(FrontReplacementSubgraph):
 
         # connect execution condition port
         exec_cond_node = Const(graph, {'name': loop_name + '/ExecutionConditionValue',
-                                       'value': mo_array(True, dtype=np.bool)}).create_node()
+                                       'value': mo_array(True, dtype=bool)}).create_node()
         loop_node.in_port(1).get_connection().set_source(exec_cond_node.out_port(0))
 
         loop_node.body.clean_up()
diff --git a/tools/mo/openvino/tools/mo/front/tf/common.py b/tools/mo/openvino/tools/mo/front/tf/common.py
index 79cfaefedf6..42a36728d66 100644
--- a/tools/mo/openvino/tools/mo/front/tf/common.py
+++ b/tools/mo/openvino/tools/mo/front/tf/common.py
@@ -7,7 +7,7 @@ from tensorflow.core.framework import types_pb2 as tf_types  # pylint: disable=n
 # Suppress false positive pylint warning about function with too many arguments
 # pylint: disable=E1121
 # mapping between TF data type and numpy data type and function to extract data from TF tensor
-_tf_np_mapping = [('DT_BOOL', np.bool, lambda pb: pb.bool_val, lambda x: bool_cast(x)),
+_tf_np_mapping = [('DT_BOOL', bool, lambda pb: pb.bool_val, lambda x: bool_cast(x)),
                   ('DT_INT8', np.int8, lambda pb: pb.int_val, lambda x: np.int8(x)),
                   ('DT_INT16', np.int16, lambda pb: pb.int_val, lambda x: np.int16(x)),
                   ('DT_INT32', np.int32, lambda pb: pb.int_val, lambda x: np.int32(x)),
@@ -32,4 +32,4 @@ def bool_cast(x):
     if isinstance(x, str):
         return False if x.lower() in ['false', '0'] else True if x.lower() in ['true', '1'] else 'unknown_boolean_cast'
     else:
-        return np.bool(x)
+        return bool(x)
diff --git a/tools/mo/openvino/tools/mo/middle/passes/convert_data_type.py b/tools/mo/openvino/tools/mo/middle/passes/convert_data_type.py
index 026c66ab28a..e7712976363 100644
--- a/tools/mo/openvino/tools/mo/middle/passes/convert_data_type.py
+++ b/tools/mo/openvino/tools/mo/middle/passes/convert_data_type.py
@@ -40,7 +40,7 @@ SUPPORTED_DATA_TYPES = {
     'int8': (np.int8, 'I8', 'i8'),
     'int32': (np.int32, 'I32', 'i32'),
     'int64': (np.int64, 'I64', 'i64'),
-    'bool': (np.bool, 'BOOL', 'boolean'),
+    'bool': (bool, 'BOOL', 'boolean'),
     'uint8': (np.uint8, 'U8', 'u8'),
     'uint32': (np.uint32, 'U32', 'u32'),
     'uint64': (np.uint64, 'U64', 'u64'),
diff --git a/tools/mo/openvino/tools/mo/ops/ReduceOps.py b/tools/mo/openvino/tools/mo/ops/ReduceOps.py
index d3414718897..f9afb3d1a2c 100644
--- a/tools/mo/openvino/tools/mo/ops/ReduceOps.py
+++ b/tools/mo/openvino/tools/mo/ops/ReduceOps.py
@@ -43,7 +43,7 @@ def reduce_helper(func: callable, x: np.array, axis: tuple, keepdims: bool):
     if is_fully_defined(x):
         return result
     else:
-        return np.ma.masked_array(result, mask=np.ones(result.shape, dtype=np.bool))
+        return np.ma.masked_array(result, mask=np.ones(result.shape, dtype=bool))
 
 
 def reduce_infer(node: Node):
@@ -73,7 +73,7 @@ def reduce_infer(node: Node):
         value = reduce_helper(reduce_map[node.op], in_value.copy(), axis=tuple(axis), keepdims=node.keep_dims)
         node.out_port(0).data.set_value(value)
     else:
-        used_dims = np.zeros(len(in_shape), dtype=np.bool)
+        used_dims = np.zeros(len(in_shape), dtype=bool)
         output_shape = in_shape.copy()
 
         for dim in axis:
diff --git a/tools/mo/openvino/tools/mo/ops/activation_ops.py b/tools/mo/openvino/tools/mo/ops/activation_ops.py
index a27f54e0fb7..5a30da8076f 100644
--- a/tools/mo/openvino/tools/mo/ops/activation_ops.py
+++ b/tools/mo/openvino/tools/mo/ops/activation_ops.py
@@ -231,7 +231,7 @@ class LogicalNot(Activation):
 
     @staticmethod
     def type_infer(node: Node):
-        node.out_port(0).set_data_type(np.bool)
+        node.out_port(0).set_data_type(bool)
 
 
 class Log(Activation):
diff --git a/tools/mo/openvino/tools/mo/ops/elementwise.py b/tools/mo/openvino/tools/mo/ops/elementwise.py
index a79d695d70a..58b2a844894 100644
--- a/tools/mo/openvino/tools/mo/ops/elementwise.py
+++ b/tools/mo/openvino/tools/mo/ops/elementwise.py
@@ -115,7 +115,7 @@ class LogicalElementwise(Elementwise):
     @staticmethod
     def type_infer(node):
         override_data_type_of_constant(node)
-        node.out_port(0).set_data_type(np.bool)
+        node.out_port(0).set_data_type(bool)
 
 
 class Greater(LogicalElementwise):
diff --git a/tools/mo/openvino/tools/mo/ops/select.py b/tools/mo/openvino/tools/mo/ops/select.py
index 57a10e3a33f..5050cd7ce0a 100644
--- a/tools/mo/openvino/tools/mo/ops/select.py
+++ b/tools/mo/openvino/tools/mo/ops/select.py
@@ -90,7 +90,7 @@ class Select(Op):
                 # one of the branches is None (which is not selected)
                 # if we use np.where for such cases then dtype of output_value will be object (non numeric type)
                 # and subsequent numpy operation on such tensors will fail
-                output_value = resulting_tensors[not np.bool(condition_value.item(0))]
+                output_value = resulting_tensors[not bool(condition_value.item(0))]
                 if output_value is None:
                     return
                 if broadcast_rule == 'numpy':
diff --git a/tools/mo/openvino/tools/mo/ops/unique.py b/tools/mo/openvino/tools/mo/ops/unique.py
index 7b4f73cd6f1..a7ebc74c5ae 100644
--- a/tools/mo/openvino/tools/mo/ops/unique.py
+++ b/tools/mo/openvino/tools/mo/ops/unique.py
@@ -152,6 +152,6 @@ class Unique(Op):
         # write result to output nodes
         j = 0
         for out_node_ind in node.out_nodes():
-            node.out_node(out_node_ind).value = mo_array(unique_output[j], dtype=np.float)
+            node.out_node(out_node_ind).value = mo_array(unique_output[j], dtype=np.float32)
             node.out_node(out_node_ind).shape = int64_array(node.out_node(out_node_ind).value.shape)
             j += 1
diff --git a/tools/mo/openvino/tools/mo/utils/ir_engine/ir_engine.py b/tools/mo/openvino/tools/mo/utils/ir_engine/ir_engine.py
index a5ec0f15827..d34b4d78072 100644
--- a/tools/mo/openvino/tools/mo/utils/ir_engine/ir_engine.py
+++ b/tools/mo/openvino/tools/mo/utils/ir_engine/ir_engine.py
@@ -346,7 +346,7 @@ class IREngine(object):
             'U1': (1, np.uint8),
             'U4': (1, np.uint8),
             'I4': (1, np.uint8),
-            'BOOL': (1, np.bool),
+            'BOOL': (1, bool),
             'BIN': (1, np.uint8),
             'U64': (8, np.uint64)
         }
diff --git a/tools/mo/openvino/tools/mo/utils/type_utils.py b/tools/mo/openvino/tools/mo/utils/type_utils.py
index ade914c0075..6ee705438f0 100644
--- a/tools/mo/openvino/tools/mo/utils/type_utils.py
+++ b/tools/mo/openvino/tools/mo/utils/type_utils.py
@@ -9,7 +9,7 @@ from openvino.tools.mo.graph.graph import Node
 from openvino.tools.mo.pipeline.common import convert_const_node_value_type
 from openvino.tools.mo.utils.error import Error
 
-np_map_cast = {np.bool: lambda x: bool_cast(x),
+np_map_cast = {bool: lambda x: bool_cast(x),
                np.int8: lambda x: np.int8(x),
                np.int16: lambda x: np.int16(x),
                np.int32: lambda x: np.int32(x),
@@ -28,7 +28,7 @@ def bool_cast(x):
     if isinstance(x, str):
         return False if x.lower() in ['false', '0'] else True if x.lower() in ['true', '1'] else 'unknown_boolean_cast'
     else:
-        return np.bool(x)
+        return bool(x)
 
 
 def override_data_type_of_constant(node: Node, lhs_idx: int = 0, rhs_idx: int = 1):
diff --git a/tools/mo/unit_tests/mo/front/kaldi/restrictedattentioncomponent_replacer_test.py b/tools/mo/unit_tests/mo/front/kaldi/restrictedattentioncomponent_replacer_test.py
index 49d526f56de..629858ca7c3 100644
--- a/tools/mo/unit_tests/mo/front/kaldi/restrictedattentioncomponent_replacer_test.py
+++ b/tools/mo/unit_tests/mo/front/kaldi/restrictedattentioncomponent_replacer_test.py
@@ -35,7 +35,7 @@ class RestrictedAttentionComponentReplacerTest(unittest.TestCase):
         **regular_op('reshape_helper_1', {'type': 'Reshape'}),
         **const('reshape_helper_1_shape', int64_array([10, 1])),
         **regular_op('mul', {'type': 'Multiply'}),
-        **const('mul_scale', mo_array(0.5, dtype=np.float)),
+        **const('mul_scale', mo_array(0.5, dtype=np.float32)),
         **regular_op('add', {'type': 'Add'}),
         **regular_op('softmax', {'type': 'SoftMax'}),
         **regular_op('reshape_helper_3', {'type': 'Reshape'}),
diff --git a/tools/mo/unit_tests/mo/front/onnx/priorbox_clustered_ext_test.py b/tools/mo/unit_tests/mo/front/onnx/priorbox_clustered_ext_test.py
index 1cc54168242..c5ec0edafce 100644
--- a/tools/mo/unit_tests/mo/front/onnx/priorbox_clustered_ext_test.py
+++ b/tools/mo/unit_tests/mo/front/onnx/priorbox_clustered_ext_test.py
@@ -46,8 +46,8 @@ class TestPriorBoxClusteredExt(unittest.TestCase):
         self.assertRaises(AttributeError, PriorBoxClusteredFrontExtractor.extract, None)
 
     def test_priorbox_clustered_ext_ideal_numbers(self):
-        node = self._create_priorbox_clustered_node(width= np.array([2, 3], dtype=np.float),
-                                          height=np.array([4, 5], dtype=np.float),
+        node = self._create_priorbox_clustered_node(width= np.array([2, 3], dtype=np.float32),
+                                          height=np.array([4, 5], dtype=np.float32),
                                           variance=np.array([0.2, 0.3, 0.2, 0.3]),
                                           img_size=300, step=5.0, offset=0.6, flip=True)
 
@@ -58,8 +58,8 @@ class TestPriorBoxClusteredExt(unittest.TestCase):
             'type': 'PriorBoxClustered',
             'clip': 0,
             'flip': 1,
-            'width': np.array([2, 3], dtype=np.float),
-            'height': np.array([4, 5], dtype=np.float),
+            'width': np.array([2, 3], dtype=np.float32),
+            'height': np.array([4, 5], dtype=np.float32),
             'variance': [0.2, 0.3, 0.2, 0.3],
             'img_size': 300,
             'img_h': 0,
diff --git a/tools/mo/unit_tests/mo/front/onnx/priorbox_ext_test.py b/tools/mo/unit_tests/mo/front/onnx/priorbox_ext_test.py
index ba67e5b0027..5cb0e799d15 100644
--- a/tools/mo/unit_tests/mo/front/onnx/priorbox_ext_test.py
+++ b/tools/mo/unit_tests/mo/front/onnx/priorbox_ext_test.py
@@ -47,7 +47,7 @@ class TestPriorBoxExt(unittest.TestCase):
         self.assertRaises(AttributeError, PriorBoxFrontExtractor.extract, None)
 
     def test_priorbox_ext_ideal_numbers(self):
-        node = self._create_priorbox_node(aspect_ratio=np.array([2, 3], dtype=np.float),
+        node = self._create_priorbox_node(aspect_ratio=np.array([2, 3], dtype=np.float32),
                                           variance=np.array([0.2, 0.3, 0.2, 0.3]),
                                           img_size=300, step=5.0, offset=0.6, flip=True)
 
@@ -58,7 +58,7 @@ class TestPriorBoxExt(unittest.TestCase):
             'type': 'PriorBox',
             'clip': 0,
             'flip': 1,
-            'aspect_ratio': np.array([2, 3], dtype=np.float),
+            'aspect_ratio': np.array([2, 3], dtype=np.float32),
             'variance': [0.2, 0.3, 0.2, 0.3],
             'img_size': 300,
             'img_h': 0,
diff --git a/tools/mo/unit_tests/mo/ops/If_test.py b/tools/mo/unit_tests/mo/ops/If_test.py
index 8199780c416..8f824a452b6 100644
--- a/tools/mo/unit_tests/mo/ops/If_test.py
+++ b/tools/mo/unit_tests/mo/ops/If_test.py
@@ -25,8 +25,8 @@ from unit_tests.utils.graph import regular_op_with_empty_data, connect, result,
 @generator
 class TestIf(unittest.TestCase):
     @generate(*[
-        (np.array([True], dtype=np.bool), shape_array([3]), shape_array([3])),
-        (np.array([False], dtype=np.bool), shape_array([3]), shape_array([2])),
+        (np.array([True], dtype=bool), shape_array([3]), shape_array([3])),
+        (np.array([False], dtype=bool), shape_array([3]), shape_array([2])),
         (shape_array(dynamic_dimension_value), shape_array([3]), shape_array([dynamic_dimension_value])),
     ])
     def test_simple_shape_inf(self, cond, output_port_0_shape, output_port_1_shape):
diff --git a/tools/mo/unit_tests/mo/ops/pad_test.py b/tools/mo/unit_tests/mo/ops/pad_test.py
index 52f3ffc4eac..a65095e026b 100644
--- a/tools/mo/unit_tests/mo/ops/pad_test.py
+++ b/tools/mo/unit_tests/mo/ops/pad_test.py
@@ -107,7 +107,7 @@ class TestPadOps(unittest.TestCase):
             nodes_with_edges_only=True,
         )
         out_shape = (1, 1, 5, 8)
-        mask = np.zeros(out_shape, dtype=np.bool)
+        mask = np.zeros(out_shape, dtype=bool)
         mask[0][0][1][2] = True
         ref_value = np.ma.masked_array(np.zeros(out_shape, dtype=np.int64), mask=mask, dtype=np.int64)
         ref_value[0][0][1][3] = 3
diff --git a/tools/mo/unit_tests/mo/ops/select_test.py b/tools/mo/unit_tests/mo/ops/select_test.py
index 9a9be434280..4d1fcc31c3d 100644
--- a/tools/mo/unit_tests/mo/ops/select_test.py
+++ b/tools/mo/unit_tests/mo/ops/select_test.py
@@ -60,16 +60,16 @@ class TestSelect(unittest.TestCase):
 
     def test_1(self):
         flag, msg = self.build_select_graph_and_infer(condition_value=np.ones([5, 6], dtype=bool),
-                                                      then_value=np.ones([5, 6], dtype=np.float),
-                                                      else_value=np.zeros([5, 6], dtype=np.float),
-                                                      out_value=np.ones([5, 6], dtype=np.float))
+                                                      then_value=np.ones([5, 6], dtype=np.float32),
+                                                      else_value=np.zeros([5, 6], dtype=np.float32),
+                                                      out_value=np.ones([5, 6], dtype=np.float32))
         self.assertTrue(flag, msg)
 
     def test_2(self):
         flag, msg = self.build_select_graph_and_infer(condition_value=np.ones([15, 3, 5], dtype=bool),
-                                                      then_value=np.ones([15, 3, 5], dtype=np.float),
-                                                      else_value=np.zeros([15, 1, 5], dtype=np.float),
-                                                      out_value=np.ones([15, 3, 5], dtype=np.float))
+                                                      then_value=np.ones([15, 3, 5], dtype=np.float32),
+                                                      else_value=np.zeros([15, 1, 5], dtype=np.float32),
+                                                      out_value=np.ones([15, 3, 5], dtype=np.float32))
         self.assertTrue(flag, msg)
 
     def test_select_infer_no_condition(self):
@@ -95,44 +95,44 @@ class TestSelect(unittest.TestCase):
 
     def test_select_infer_condition_true_2(self):
         flag, msg = self.build_select_graph_and_infer(condition_value=np.array([True], dtype=bool),
-                                                      then_value=np.ones([15, 3, 5], dtype=np.float),
-                                                      else_value=np.zeros([15, 1, 5], dtype=np.float),
-                                                      out_value=np.ones([15, 3, 5], dtype=np.float))
+                                                      then_value=np.ones([15, 3, 5], dtype=np.float32),
+                                                      else_value=np.zeros([15, 1, 5], dtype=np.float32),
+                                                      out_value=np.ones([15, 3, 5], dtype=np.float32))
         self.assertTrue(flag, msg)
 
     def test_select_infer_condition_true_then_and_else_are_scalars(self):
         flag, msg = self.build_select_graph_and_infer(condition_value=np.array([True], dtype=bool),
-                                                      then_value=np.array(3, dtype=np.float),
-                                                      else_value=np.array(1, dtype=np.float),
-                                                      out_value=np.array([3], dtype=np.float))
+                                                      then_value=np.array(3, dtype=np.float32),
+                                                      else_value=np.array(1, dtype=np.float32),
+                                                      out_value=np.array([3], dtype=np.float32))
         self.assertTrue(flag, msg)
 
     def test_select_infer_condition_true_then_and_else_are_scalars_2(self):
         flag, msg = self.build_select_graph_and_infer(condition_value=np.array(True, dtype=bool),
-                                                      then_value=np.array(3, dtype=np.float),
-                                                      else_value=np.array(1, dtype=np.float),
-                                                      out_value=np.array(3, dtype=np.float))
+                                                      then_value=np.array(3, dtype=np.float32),
+                                                      else_value=np.array(1, dtype=np.float32),
+                                                      out_value=np.array(3, dtype=np.float32))
         self.assertTrue(flag, msg)
 
     def test_select_infer_condition_false_then_and_else_are_scalars(self):
         flag, msg = self.build_select_graph_and_infer(condition_value=np.array([False], dtype=bool),
-                                                      then_value=np.array(3, dtype=np.float),
-                                                      else_value=np.array(1, dtype=np.float),
-                                                      out_value=np.array([1], dtype=np.float))
+                                                      then_value=np.array(3, dtype=np.float32),
+                                                      else_value=np.array(1, dtype=np.float32),
+                                                      out_value=np.array([1], dtype=np.float32))
         self.assertTrue(flag, msg)
 
     def test_select_infer_condition_false_then_and_else_are_scalars_2(self):
         flag, msg = self.build_select_graph_and_infer(condition_value=np.array(False, dtype=bool),
-                                                      then_value=np.array(3, dtype=np.float),
-                                                      else_value=np.array(1, dtype=np.float),
-                                                      out_value=np.array(1, dtype=np.float))
+                                                      then_value=np.array(3, dtype=np.float32),
+                                                      else_value=np.array(1, dtype=np.float32),
+                                                      out_value=np.array(1, dtype=np.float32))
         self.assertTrue(flag, msg)
 
     def test_select_infer_condition_false_2(self):
         flag, msg = self.build_select_graph_and_infer(condition_value=np.array([False], dtype=bool),
-                                                      then_value=np.ones([15, 3, 5], dtype=np.float),
-                                                      else_value=np.zeros([15, 1, 5], dtype=np.float),
-                                                      out_value=np.zeros([15, 3, 5], dtype=np.float))
+                                                      then_value=np.ones([15, 3, 5], dtype=np.float32),
+                                                      else_value=np.zeros([15, 1, 5], dtype=np.float32),
+                                                      out_value=np.zeros([15, 3, 5], dtype=np.float32))
         self.assertTrue(flag, msg)
 
     # if one of the branches is None then np.where shouldn't be used to avoid object dtype in output
@@ -142,57 +142,57 @@ class TestSelect(unittest.TestCase):
     def test_select_infer_None_then_branch_1(self):
         flag, msg = self.build_select_graph_and_infer(condition_value=np.zeros([15, 3, 5], dtype=bool),
                                                       then_value=None, then_shape=[15, 3, 5],
-                                                      else_value=np.ones([15, 1, 5], dtype=np.float),
-                                                      out_value=np.ones([15, 3, 5], dtype=np.float))
+                                                      else_value=np.ones([15, 1, 5], dtype=np.float32),
+                                                      out_value=np.ones([15, 3, 5], dtype=np.float32))
         self.assertTrue(flag, msg)
 
     def test_select_infer_None_then_branch_2(self):
         flag, msg = self.build_select_graph_and_infer(condition_value=np.ones([15, 3, 5], dtype=bool),
                                                       then_value=None, then_shape=[15, 3, 5],
-                                                      else_value=np.ones([15, 1, 5], dtype=np.float),
+                                                      else_value=np.ones([15, 1, 5], dtype=np.float32),
                                                       out_value=None)
         self.assertTrue(flag, msg)
 
     def test_select_infer_None_else_branch_1(self):
         flag, msg = self.build_select_graph_and_infer(condition_value=np.ones([15, 3, 5], dtype=bool),
-                                                      then_value=np.ones([15, 1, 5], dtype=np.float),
+                                                      then_value=np.ones([15, 1, 5], dtype=np.float32),
                                                       else_value=None, else_shape=[15, 3, 5],
-                                                      out_value=np.ones([15, 3, 5], dtype=np.float))
+                                                      out_value=np.ones([15, 3, 5], dtype=np.float32))
         self.assertTrue(flag, msg)
 
     def test_select_infer_None_else_branch_2(self):
         flag, msg = self.build_select_graph_and_infer(condition_value=np.zeros([15, 3, 5], dtype=bool),
-                                                      then_value=np.ones([15, 1, 5], dtype=np.float),
+                                                      then_value=np.ones([15, 1, 5], dtype=np.float32),
                                                       else_value=None, else_shape=[15, 3, 5],
                                                       out_value=None)
         self.assertTrue(flag, msg)
 
     def test_select_broadcast_1(self):
         flag, msg = self.build_select_graph_and_infer(condition_value=np.ones([2, 3, 4, 5], dtype=bool),
-                                                      then_value=np.ones([], dtype=np.float),
-                                                      else_value=np.zeros([2, 3, 4, 5], dtype=np.float),
-                                                      out_value=np.ones([2, 3, 4, 5], dtype=np.float))
+                                                      then_value=np.ones([], dtype=np.float32),
+                                                      else_value=np.zeros([2, 3, 4, 5], dtype=np.float32),
+                                                      out_value=np.ones([2, 3, 4, 5], dtype=np.float32))
         self.assertTrue(flag, msg)
 
     def test_select_broadcast_2(self):
         flag, msg = self.build_select_graph_and_infer(condition_value=np.ones([2, 3, 4, 1], dtype=bool),
-                                                      then_value= np.ones([1, 3, 1, 5], dtype=np.float),
-                                                      else_value=np.zeros([2, 1, 1, 5], dtype=np.float),
-                                                      out_value=np.ones([2, 3, 4, 5], dtype=np.float))
+                                                      then_value= np.ones([1, 3, 1, 5], dtype=np.float32),
+                                                      else_value=np.zeros([2, 1, 1, 5], dtype=np.float32),
+                                                      out_value=np.ones([2, 3, 4, 5], dtype=np.float32))
         self.assertTrue(flag, msg)
 
     def test_select_broadcast_3(self):
         flag, msg = self.build_select_graph_and_infer(condition_value=np.ones([2, 3, 1, 1], dtype=bool),
-                                                      then_value= np.ones([2, 3, 4, 5], dtype=np.float),
-                                                      else_value=np.zeros([2, 1, 1, 5], dtype=np.float),
-                                                      out_value=np.ones([2, 3, 4, 5], dtype=np.float))
+                                                      then_value= np.ones([2, 3, 4, 5], dtype=np.float32),
+                                                      else_value=np.zeros([2, 1, 1, 5], dtype=np.float32),
+                                                      out_value=np.ones([2, 3, 4, 5], dtype=np.float32))
         self.assertTrue(flag, msg)
 
     def test_select_broadcast_4(self):
         flag, msg = self.build_select_graph_and_infer(condition_value=np.ones([2, 3, 4, 5], dtype=bool),
-                                                      then_value= np.ones([5], dtype=np.float),
-                                                      else_value=np.zeros([2, 3, 4, 5], dtype=np.float),
-                                                      out_value=np.ones([2, 3, 4, 5], dtype=np.float))
+                                                      then_value= np.ones([5], dtype=np.float32),
+                                                      else_value=np.zeros([2, 3, 4, 5], dtype=np.float32),
+                                                      out_value=np.ones([2, 3, 4, 5], dtype=np.float32))
         self.assertTrue(flag, msg)
 
     # when output shape is broadcasted from condition, then, and else shapes
diff --git a/tools/mo/unit_tests/mo/ops/sparse_segment_mean_test.py b/tools/mo/unit_tests/mo/ops/sparse_segment_mean_test.py
index 93a1e078ad2..a4b69a18827 100644
--- a/tools/mo/unit_tests/mo/ops/sparse_segment_mean_test.py
+++ b/tools/mo/unit_tests/mo/ops/sparse_segment_mean_test.py
@@ -40,9 +40,9 @@ edges2 = [('input_data', 'sparse_segment_mean_node', {'in': 0}),
           ('input_segment_ids', 'sparse_segment_mean_node', {'in': 2}),
           ('sparse_segment_mean_node', 'output_segments', {'out': 0})]
 
-inputs2 = {'input_data': {'shape': int64_array([3, 4]), 'value': np.array([[1, 2, 3, 4], [-1, -2, -3, -4], [5, 6, 7, 8]], dtype=np.float)},
-           'input_indices': {'shape': int64_array([3]), 'value': np.array([0, 2, 1, 1, 2], dtype=np.float)},
-           'input_segment_ids': {'shape': int64_array([3]), 'value': np.array([0, 0, 1, 2, 2], dtype=np.float)}}
+inputs2 = {'input_data': {'shape': int64_array([3, 4]), 'value': np.array([[1, 2, 3, 4], [-1, -2, -3, -4], [5, 6, 7, 8]], dtype=np.float32)},
+           'input_indices': {'shape': int64_array([3]), 'value': np.array([0, 2, 1, 1, 2], dtype=np.float32)},
+           'input_segment_ids': {'shape': int64_array([3]), 'value': np.array([0, 0, 1, 2, 2], dtype=np.float32)}}
 
 class TestSparseSegmentMean(unittest.TestCase):
     def test_partial_infer(self):
@@ -76,7 +76,7 @@ class TestSparseSegmentMean(unittest.TestCase):
 
         # prepare reference results
         ref_output_segments_shape = int64_array([3, 4])
-        ref_output_segments_value = np.array([[3, 4, 5, 6], [-1, -2, -3, -4], [2, 2, 2, 2]], dtype=np.float)
+        ref_output_segments_value = np.array([[3, 4, 5, 6], [-1, -2, -3, -4], [2, 2, 2, 2]], dtype=np.float32)
 
         # get resulted shapes
         res_output_segments_shape = graph.node['output_segments']['shape']
diff --git a/tools/mo/unit_tests/mo/ops/sparse_segment_sqrtn_test.py b/tools/mo/unit_tests/mo/ops/sparse_segment_sqrtn_test.py
index de5b6032ecf..ad65c4a5b86 100644
--- a/tools/mo/unit_tests/mo/ops/sparse_segment_sqrtn_test.py
+++ b/tools/mo/unit_tests/mo/ops/sparse_segment_sqrtn_test.py
@@ -40,9 +40,9 @@ edges2 = [('input_data', 'sparse_segment_sqrtn_node', {'in': 0}),
           ('input_segment_ids', 'sparse_segment_sqrtn_node', {'in': 2}),
           ('sparse_segment_sqrtn_node', 'output_segments', {'out': 0})]
 
-inputs2 = {'input_data': {'shape': int64_array([3, 4]), 'value': np.array([[1, 2, 3, 4], [-1, -2, -3, -4], [5, 6, 7, 8]], dtype=np.float)},
-           'input_indices': {'shape': int64_array([3]), 'value': np.array([0, 2, 1, 1, 2], dtype=np.float)},
-           'input_segment_ids': {'shape': int64_array([3]), 'value': np.array([0, 0, 0, 0, 2], dtype=np.float)}}
+inputs2 = {'input_data': {'shape': int64_array([3, 4]), 'value': np.array([[1, 2, 3, 4], [-1, -2, -3, -4], [5, 6, 7, 8]], dtype=np.float32)},
+           'input_indices': {'shape': int64_array([3]), 'value': np.array([0, 2, 1, 1, 2], dtype=np.float32)},
+           'input_segment_ids': {'shape': int64_array([3]), 'value': np.array([0, 0, 0, 0, 2], dtype=np.float32)}}
 
 class TestSparseSegmentSqrtN(unittest.TestCase):
     def test_partial_infer(self):
@@ -76,7 +76,7 @@ class TestSparseSegmentSqrtN(unittest.TestCase):
 
         # prepare reference results
         ref_output_segments_shape = int64_array([3, 4])
-        ref_output_segments_value = np.array([[2, 2, 2, 2], [0, 0, 0, 0], [5, 6, 7, 8]], dtype=np.float)
+        ref_output_segments_value = np.array([[2, 2, 2, 2], [0, 0, 0, 0], [5, 6, 7, 8]], dtype=np.float32)
 
         # get resulted shapes
         res_output_segments_shape = graph.node['output_segments']['shape']
diff --git a/tools/mo/unit_tests/mo/ops/sparse_segment_sum_test.py b/tools/mo/unit_tests/mo/ops/sparse_segment_sum_test.py
index 606fe91fe15..f6f818ad602 100644
--- a/tools/mo/unit_tests/mo/ops/sparse_segment_sum_test.py
+++ b/tools/mo/unit_tests/mo/ops/sparse_segment_sum_test.py
@@ -40,9 +40,9 @@ edges2 = [('input_data', 'sparse_segment_sum_node', {'in': 0}),
           ('input_segment_ids', 'sparse_segment_sum_node', {'in': 2}),
           ('sparse_segment_sum_node', 'output_segments', {'out': 0})]
 
-inputs2 = {'input_data': {'shape': int64_array([3, 4]), 'value': np.array([[1, 2, 3, 4], [-1, -2, -3, -4], [5, 6, 7, 8]], dtype=np.float)},
-           'input_indices': {'shape': int64_array([3]), 'value': np.array([0, 1, 2], dtype=np.float)},
-           'input_segment_ids': {'shape': int64_array([3]), 'value': np.array([0, 0, 1], dtype=np.float)}}
+inputs2 = {'input_data': {'shape': int64_array([3, 4]), 'value': np.array([[1, 2, 3, 4], [-1, -2, -3, -4], [5, 6, 7, 8]], dtype=np.float32)},
+           'input_indices': {'shape': int64_array([3]), 'value': np.array([0, 1, 2], dtype=np.float32)},
+           'input_segment_ids': {'shape': int64_array([3]), 'value': np.array([0, 0, 1], dtype=np.float32)}}
 
 class TestSparseSegmentSum(unittest.TestCase):
     def test_partial_infer(self):
@@ -76,7 +76,7 @@ class TestSparseSegmentSum(unittest.TestCase):
 
         # prepare reference results
         ref_output_segments_shape = int64_array([2, 4])
-        ref_output_segments_value = np.array([[0, 0, 0, 0], [5, 6, 7, 8]], dtype=np.float)
+        ref_output_segments_value = np.array([[0, 0, 0, 0], [5, 6, 7, 8]], dtype=np.float32)
 
         # get resulted shapes
         res_output_segments_shape = graph.node['output_segments']['shape']
diff --git a/tools/mo/unit_tests/mo/ops/unique_test.py b/tools/mo/unit_tests/mo/ops/unique_test.py
index d4465d90b42..5cc2b269e15 100644
--- a/tools/mo/unit_tests/mo/ops/unique_test.py
+++ b/tools/mo/unit_tests/mo/ops/unique_test.py
@@ -162,7 +162,7 @@ class TestUnique(unittest.TestCase):
                   ('unique_node', 'output_indices', {'out': 1}),
                   ('unique_node', 'output_counts', {'out': 2})]
         inputs_ = {'input': {'shape': int64_array([10]),
-                             'value': np.array([8.0, 1.0, 2.0, 1.0, 8.0, 5.0, 1.0, 5.0, 0.0, 0.0], dtype=np.float)},
+                             'value': np.array([8.0, 1.0, 2.0, 1.0, 8.0, 5.0, 1.0, 5.0, 0.0, 0.0], dtype=np.float32)},
                    'unique_node': {
                        'sorted': 'false',
                        'return_inverse': 'true',
@@ -175,11 +175,11 @@ class TestUnique(unittest.TestCase):
 
         # prepare reference results
         ref_output_uniques_shape = int64_array([5])
-        ref_output_uniques_value = np.array([8.0, 1.0, 2.0, 5.0, 0.0], dtype=np.float)
+        ref_output_uniques_value = np.array([8.0, 1.0, 2.0, 5.0, 0.0], dtype=np.float32)
         ref_output_indices_shape = int64_array([10])
-        ref_output_indices_value = np.array([0.0, 1.0, 2.0, 1.0, 0.0, 3.0, 1.0, 3.0, 4.0, 4.0], dtype=np.float)
+        ref_output_indices_value = np.array([0.0, 1.0, 2.0, 1.0, 0.0, 3.0, 1.0, 3.0, 4.0, 4.0], dtype=np.float32)
         ref_output_counts_shape = int64_array([5])
-        ref_output_counts_value = np.array([2.0, 3.0, 1.0, 2.0, 2.0], dtype=np.float)
+        ref_output_counts_value = np.array([2.0, 3.0, 1.0, 2.0, 2.0], dtype=np.float32)
 
         # get resulted shapes
         res_output_uniques_shape = graph.node['output_uniques']['shape']
@@ -217,7 +217,7 @@ class TestUnique(unittest.TestCase):
                   ('unique_node', 'output_indices', {'out': 1}),
                   ('unique_node', 'output_counts', {'out': 2})]
         inputs_ = {'input': {'shape': int64_array([10]),
-                             'value': np.array([8.0, 1.0, 2.0, 1.0, 8.0, 5.0, 1.0, 5.0, 0.0, 0.0], dtype=np.float)},
+                             'value': np.array([8.0, 1.0, 2.0, 1.0, 8.0, 5.0, 1.0, 5.0, 0.0, 0.0], dtype=np.float32)},
                    'unique_node': {
                        'sorted': 'true',
                        'return_inverse': 'true',
@@ -230,11 +230,11 @@ class TestUnique(unittest.TestCase):
 
         # prepare reference results
         ref_output_uniques_shape = int64_array([5])
-        ref_output_uniques_value = np.array([0.0, 1.0, 2.0, 5.0, 8.0], dtype=np.float)
+        ref_output_uniques_value = np.array([0.0, 1.0, 2.0, 5.0, 8.0], dtype=np.float32)
         ref_output_indices_shape = int64_array([10])
-        ref_output_indices_value = np.array([4.0, 1.0, 2.0, 1.0, 4.0, 3.0, 1.0, 3.0, 0.0, 0.0], dtype=np.float)
+        ref_output_indices_value = np.array([4.0, 1.0, 2.0, 1.0, 4.0, 3.0, 1.0, 3.0, 0.0, 0.0], dtype=np.float32)
         ref_output_counts_shape = int64_array([5])
-        ref_output_counts_value = np.array([2.0, 3.0, 1.0, 2.0, 2.0], dtype=np.float)
+        ref_output_counts_value = np.array([2.0, 3.0, 1.0, 2.0, 2.0], dtype=np.float32)
 
         # get resulted shapes
         res_output_uniques_shape = graph.node['output_uniques']['shape']
diff --git a/tools/mo/unit_tests/mo/utils/args_to_string_test.py b/tools/mo/unit_tests/mo/utils/args_to_string_test.py
index 8c6db084091..74cbf17d48e 100644
--- a/tools/mo/unit_tests/mo/utils/args_to_string_test.py
+++ b/tools/mo/unit_tests/mo/utils/args_to_string_test.py
@@ -36,7 +36,7 @@ class TestConvertingConvertArgumentsToString(UnitTestWithMockedTelemetry):
         inp8 = InputCutInfo("data2", [4, 5, 6], np.int64, [5, 4, 3, 2, 1])
         self.assertTrue(input_to_str(inp8) == "data2[4 5 6]{i64}->[5 4 3 2 1]")
 
-        inp9 = InputCutInfo("data", [1], np.bool, True)
+        inp9 = InputCutInfo("data", [1], bool, True)
         self.assertTrue(input_to_str(inp9) == "data[1]{boolean}->True")
 
         inp = [inp6, inp7, inp8]
@@ -114,7 +114,7 @@ class TestConvertingConvertArgumentsToString(UnitTestWithMockedTelemetry):
         self.assertRaises(Exception, input_to_str, **{"input": ("name", [np.int, 2, 3])})
         self.assertRaises(Exception, input_to_str, **{"input": ("name", "name1", [2, 3])})
         self.assertRaises(Exception, input_to_str, **{"input": ("name", [2, 3], Shape([1, 2]))})
-        self.assertRaises(Exception, input_to_str, **{"input": ("name", np.int, Type(np.float))})
+        self.assertRaises(Exception, input_to_str, **{"input": ("name", np.int, Type(np.float32))})
         self.assertRaises(Exception, input_to_str, **{"input": Exception})
         self.assertRaises(Exception, input_to_str, **{"input": ("name", Exception)})
         self.assertRaises(Exception, input_to_str, **{"input": ("name", Dimension(1))})
diff --git a/tools/pot/openvino/tools/pot/api/samples/face_detection/face_detection_sample.py b/tools/pot/openvino/tools/pot/api/samples/face_detection/face_detection_sample.py
index 66372cade68..aa7e76ed1b2 100644
--- a/tools/pot/openvino/tools/pot/api/samples/face_detection/face_detection_sample.py
+++ b/tools/pot/openvino/tools/pot/api/samples/face_detection/face_detection_sample.py
@@ -229,7 +229,7 @@ class MTCNNEngine(IEEngine):
 
         def postprocess(output):
             # extract_predictions
-            total_boxes = np.zeros((0, 9), np.float)
+            total_boxes = np.zeros((0, 9), np.float32)
             for idx, outputs in enumerate(output):
                 scales = input_meta['scales'][idx]
                 mapping = outputs[[i for i, _ in outputs.items()
diff --git a/tools/pot/openvino/tools/pot/api/samples/object_detection/metric.py b/tools/pot/openvino/tools/pot/api/samples/object_detection/metric.py
index 0e9eb14688f..b1f8380fcef 100644
--- a/tools/pot/openvino/tools/pot/api/samples/object_detection/metric.py
+++ b/tools/pot/openvino/tools/pot/api/samples/object_detection/metric.py
@@ -59,8 +59,8 @@ class MAP(Metric):
         npig = np.count_nonzero(gt_ignored == 0)
         tps = np.logical_and(dtm, np.logical_not(dt_ignored))
         fps = np.logical_and(np.logical_not(dtm), np.logical_not(dt_ignored))
-        tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float)
-        fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float)
+        tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float32)
+        fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float32)
         if npig == 0:
             return np.nan, np.nan
         for t, (tp, fp) in enumerate(zip(tp_sum, fp_sum)):
diff --git a/tools/pot/tests/test_tensor_statistics.py b/tools/pot/tests/test_tensor_statistics.py
index 6b7880da8d4..8370784e5c5 100644
--- a/tools/pot/tests/test_tensor_statistics.py
+++ b/tools/pot/tests/test_tensor_statistics.py
@@ -20,7 +20,7 @@ GOLD_VALUES_AGGREGATION_FUNCTIONS_2_2_1 = {
     'mean_no_outliers': 1.5,
     'median_no_outliers': 1.5,
     'hl_estimator': 1.5,
-    'batch_mean': np.array([[[1], [2]]], dtype=np.float)
+    'batch_mean': np.array([[[1], [2]]], dtype=np.float32)
 }
 
 GOLD_VALUES_AGGREGATION_FUNCTIONS_2_2_2 = {
@@ -31,7 +31,7 @@ GOLD_VALUES_AGGREGATION_FUNCTIONS_2_2_2 = {
     'mean_no_outliers': [3., 4.],
     'median_no_outliers': [3., 4.],
     'hl_estimator': [3., 4.],
-    'batch_mean': np.array([[[2, 3], [4, 5]]], dtype=np.float)
+    'batch_mean': np.array([[[2, 3], [4, 5]]], dtype=np.float32)
 }
 
 GOLD_VALUES_AGGREGATION_FUNCTIONS = [