diff --git a/src/plugins/intel_gpu/src/graph/one_hot.cpp b/src/plugins/intel_gpu/src/graph/one_hot.cpp
index e16044d9f6c..ba0254b1227 100644
--- a/src/plugins/intel_gpu/src/graph/one_hot.cpp
+++ b/src/plugins/intel_gpu/src/graph/one_hot.cpp
@@ -104,6 +104,9 @@ std::string one_hot_inst::to_string(one_hot_node const& node) {
 one_hot_inst::typed_primitive_inst(network& network, one_hot_node const& node) : parent(network, node) {
     auto input_layout = node.input().get_output_layout();
 
+    if (input_layout.is_dynamic())
+        return;
+
     const auto& input_sizes = input_layout.get_tensor();
     const auto& output_sizes = argument.shape;
 
diff --git a/src/plugins/intel_gpu/src/graph/reorder.cpp b/src/plugins/intel_gpu/src/graph/reorder.cpp
index 6f1e9efeaab..511a6ccb281 100644
--- a/src/plugins/intel_gpu/src/graph/reorder.cpp
+++ b/src/plugins/intel_gpu/src/graph/reorder.cpp
@@ -200,7 +200,7 @@ std::string reorder_inst::to_string(reorder_node const& node) {
 }
 
 reorder_inst::typed_primitive_inst(network& network, reorder_node const& node)
-    : parent(network, node, !node.can_be_optimized() && !node.is_dynamic()) {
+    : parent(network, node, (!node.can_be_optimized() && node.get_output_layout().is_static()) ? true : false) {
     if (node.can_be_optimized())
         reuse_input();
 
diff --git a/src/plugins/intel_gpu/src/graph/reshape.cpp b/src/plugins/intel_gpu/src/graph/reshape.cpp
index 9268ead8cd7..bdf68b3bcc6 100644
--- a/src/plugins/intel_gpu/src/graph/reshape.cpp
+++ b/src/plugins/intel_gpu/src/graph/reshape.cpp
@@ -60,7 +60,14 @@ std::vector<layout> reshape_inst::calc_output_layouts(reshape_node const& /*node
     // we return output_partial_shape taken from the original model intead of something like PartialShape::dynamic(rank)
     // as ngraph may refine output shape using interval arithmetic
     if ((memory_deps.empty() && prim->output_pattern.empty()) || input_layout.is_dynamic()) {
-        return { layout{prim->output_partial_shape, input_layout.data_type, format::adjust_to_rank(input_layout.format, prim->output_partial_shape.size())} };
+        if (prim->output_partial_shape.size() > 0) {
+            auto fm = format::adjust_to_rank(input_layout.format, prim->output_partial_shape.size());
+            return { layout{prim->output_partial_shape, input_layout.data_type, fm} };
+        } else if (prim->output_shape != tensor()) {
+            return { layout{input_layout.data_type, input_layout.format, prim->output_shape} };
+        } else {
+            OPENVINO_ASSERT("There are no output pattern, predefined output partial shape, and output shape!");
+        }
     }
 
     ShapeType pattern_shape = impl_param.input_layouts.size() == 2 ? impl_param.get_input_layout(1).get<ShapeType>()
@@ -78,16 +85,19 @@ std::vector<layout> reshape_inst::calc_output_layouts(reshape_node const& /*node
             case reshape::reshape_mode::base: {
                 ov::op::v1::Reshape op;
                 op.set_special_zero(prim->special_zero);
+                op.set_friendly_name(prim->id.c_str());
                 shape_infer(&op, input_shapes, output_shapes, const_data);
                 break;
             }
             case reshape::reshape_mode::squeeze: {
                 ov::op::v0::Squeeze op;
+                op.set_friendly_name(prim->id.c_str());
                 shape_infer(&op, input_shapes, output_shapes, const_data);
                 break;
             }
             case reshape::reshape_mode::unsqueeze: {
                 ov::op::v0::Unsqueeze op;
+                op.set_friendly_name(prim->id.c_str());
                 shape_infer(&op, input_shapes, output_shapes, const_data);
                 break;
             }
@@ -114,7 +124,7 @@ std::vector<layout> reshape_inst::calc_output_layouts(reshape_node const& /*node
         run_shape_infer(prim->mode);
     }
 
-    return { layout{output_shapes[0], input_layout.data_type, format::adjust_to_rank(input_layout.format, output_shapes[0].size())} };
+    return { layout {output_shapes[0], input_layout.data_type, format::adjust_to_rank(input_layout.format, output_shapes[0].size())} };
 }
 
 template std::vector<layout> reshape_inst::calc_output_layouts<ov::PartialShape>(reshape_node const& node, const kernel_impl_params& impl_param);
diff --git a/src/plugins/intel_gpu/src/plugin/ops/one_hot.cpp b/src/plugins/intel_gpu/src/plugin/ops/one_hot.cpp
index 458714813e3..277dbe1a4a9 100644
--- a/src/plugins/intel_gpu/src/plugin/ops/one_hot.cpp
+++ b/src/plugins/intel_gpu/src/plugin/ops/one_hot.cpp
@@ -52,10 +52,7 @@ static void CreateOneHotOp(Program& p, const std::shared_ptr<ngraph::op::v1::One
     int64_t depth = depth_value_node->cast_vector<int64_t>()[0];
 
     auto out_pshape = op->get_output_partial_shape(0);
-    if (out_pshape.is_dynamic()) {
-        IE_THROW() << "OneHot doesn't support dynamic shapes yet";
-    }
-    auto out_tensor = tensor_from_dims(out_pshape.to_shape());
+    cldnn::tensor out_tensor = out_pshape.is_static() ? tensor_from_dims(out_pshape.to_shape()) : cldnn::tensor{};
 
     auto oneHotPrim = cldnn::one_hot(layerName,
                                      inputPrimitives[0],
diff --git a/src/plugins/intel_gpu/src/plugin/ops/strided_slice.cpp b/src/plugins/intel_gpu/src/plugin/ops/strided_slice.cpp
index 0823f51cca0..8bbab32fef9 100644
--- a/src/plugins/intel_gpu/src/plugin/ops/strided_slice.cpp
+++ b/src/plugins/intel_gpu/src/plugin/ops/strided_slice.cpp
@@ -227,9 +227,14 @@ static void CreateStridedSliceOp(Program& p, const std::shared_ptr<ngraph::op::v
 
         // Reshape in case of deleting of axis
         if (!shrink_axis_mask.empty()) {
-            auto targetShape = tensor_from_dims(output_shape);
+            std::vector<int64_t> output_pattern(output_shape.size());
+            auto out_p = output_pattern.begin();
+            for (auto s = output_shape.begin(); s != output_shape.end() && out_p != output_pattern.end(); s++, out_p++) {
+                *out_p = *s;
+            }
+
             auto reshapeOutName = op->get_friendly_name() + "/Crop";
-            auto reshapePrim = cldnn::reshape(reshapeOutName, layerName, targetShape);
+            auto reshapePrim = cldnn::reshape(reshapeOutName, layerName, false, output_pattern, output_pshape);
             p.add_primitive(*op, reshapePrim);
             last_layer_primitive = reshapeOutName;
         }