diff --git a/src/frontends/pytorch/src/op/log_softmax.cpp b/src/frontends/pytorch/src/op/log_softmax.cpp
new file mode 100644
index 00000000000..1baaf428981
--- /dev/null
+++ b/src/frontends/pytorch/src/op/log_softmax.cpp
@@ -0,0 +1,44 @@
+// Copyright (C) 2018-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "openvino/frontend/pytorch/node_context.hpp"
+#include "openvino/opsets/opset10.hpp"
+#include "utils.hpp"
+
+namespace ov {
+namespace frontend {
+namespace pytorch {
+namespace op {
+
+using namespace ov::op;
+
+OutputVector translate_log_softmax(const NodeContext& context) {
+    /*
+    aten::log_softmax(
+        Tensor input,
+        int64 dim,
+        dtype dtype = None
+    )
+    */
+    num_inputs_check(context, 2, 3);
+    auto input = context.get_input(0);
+    auto const dim = context.const_input<int64_t>(1);
+
+    if (!context.input_is_none(2)) {
+        const auto elem_type = input.get_element_type();
+        const auto target_dtype_i64 = context.const_input<int64_t>(2);
+        const auto target_dtype = convert_dtype(target_dtype_i64);
+        if (elem_type != target_dtype) {
+            input = context.mark_node(std::make_shared<opset10::Convert>(input, target_dtype));
+        }
+    }
+
+    const auto log_softmax = context.mark_node(std::make_shared<opset10::LogSoftmax>(input, dim));
+    return {log_softmax};
+};
+
+}  // namespace op
+}  // namespace pytorch
+}  // namespace frontend
+}  // namespace ov
diff --git a/src/frontends/pytorch/src/op_table.cpp b/src/frontends/pytorch/src/op_table.cpp
index cbb65bf6449..c0e00c0dd5b 100644
--- a/src/frontends/pytorch/src/op_table.cpp
+++ b/src/frontends/pytorch/src/op_table.cpp
@@ -79,6 +79,7 @@ OP_CONVERTER(translate_linalg_vector_norm);
 OP_CONVERTER(translate_linear);
 OP_CONVERTER(translate_list_construct);
 OP_CONVERTER(translate_log);
+OP_CONVERTER(translate_log_softmax);
 OP_CONVERTER(translate_log2);
 OP_CONVERTER(translate_loop);
 OP_CONVERTER(translate_masked_fill);
@@ -273,6 +274,7 @@ const std::map<std::string, CreatorFunction> get_supported_ops() {
         {"aten::linear", op::translate_linear},
         {"aten::log", op::translate_log},
         {"aten::log_", op::inplace_op<op::translate_log>},
+        {"aten::log_softmax", op::translate_log_softmax},
         {"aten::log2", op::translate_log2},
         {"aten::log2_", op::inplace_op<op::translate_log2>},
         {"aten::lt", op::translate_1to1_match_2_inputs_align_types<opset10::Less>},
diff --git a/tests/layer_tests/pytorch_tests/test_log_softmax.py b/tests/layer_tests/pytorch_tests/test_log_softmax.py
new file mode 100644
index 00000000000..de9ce28fded
--- /dev/null
+++ b/tests/layer_tests/pytorch_tests/test_log_softmax.py
@@ -0,0 +1,45 @@
+# Copyright (C) 2018-2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np
+import pytest
+import torch
+import torch.nn.functional as F
+
+from pytorch_layer_test_class import PytorchLayerTest
+
+class aten_log_softmax(torch.nn.Module):
+    def __init__(self, dim, dtype) -> None:
+        super().__init__()
+        self.dim = dim
+        self.dtype = dtype
+
+    def forward(self, input_tensor):
+        return F.log_softmax(input_tensor, dim = self.dim, dtype = self.dtype)
+
+class TestLogSoftmax(PytorchLayerTest):
+    def _prepare_input(self):
+        if self.input_dtype == torch.float:
+            self.input_tensor = np.random.randn(5, 9, 7)
+        else:
+            self.input_tensor = np.random.randint(-100, 100, (5, 9, 7))
+        return (self.input_tensor,)
+
+    @pytest.mark.parametrize(["input_dtype", "convert_dtype"], [
+        # convert_dtype cannot be of type int from pytorch limitations
+        [torch.int,   torch.float32],
+        [torch.int,   torch.float64],
+        [torch.float, None],
+        [torch.float, torch.float64]
+    ])
+    @pytest.mark.parametrize("dim", [
+        0,
+        1, 
+        -1
+    ])
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    def test_log_softmax(self, input_dtype, convert_dtype, dim, ie_device, precision, ir_version):
+        self.input_dtype = input_dtype
+        self._test(aten_log_softmax(dim, convert_dtype), None, "aten::log_softmax", 
+                    ie_device, precision, ir_version)