diff --git a/src/frontends/pytorch/src/op/topk.cpp b/src/frontends/pytorch/src/op/topk.cpp
new file mode 100644
index 00000000000..7dfc7af5226
--- /dev/null
+++ b/src/frontends/pytorch/src/op/topk.cpp
@@ -0,0 +1,43 @@
+// Copyright (C) 2018-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "openvino/op/topk.hpp"
+
+#include "openvino/frontend/pytorch/node_context.hpp"
+#include "openvino/op/convert.hpp"
+
+namespace ov {
+namespace frontend {
+namespace pytorch {
+namespace op {
+
+OutputVector translate_topk(NodeContext& context) {
+    const auto input_tensor = context.get_input(0);
+    const auto largest = context.const_input<bool>(3);
+    const auto sorted = context.const_input<bool>(4);
+    auto k = context.get_input(1);
+    int64_t axis{-1};
+    auto mode = ov::op::TopKMode::MIN;
+    auto sort = ov::op::TopKSortType::NONE;
+
+    if (!context.input_is_none(2)) {
+        axis = context.const_input<int64_t>(2);
+    }
+    if (largest) {
+        mode = ov::op::TopKMode::MAX;
+    }
+    if (sorted) {
+        sort = ov::op::TopKSortType::SORT_VALUES;
+    }
+
+    auto topk = context.mark_node(std::make_shared<ov::op::v3::TopK>(input_tensor, k, axis, mode, sort));
+    auto indices = context.mark_node(std::make_shared<ov::op::v0::Convert>(topk->output(1), element::i64));
+
+    return {topk->output(0), indices};
+};
+
+}  // 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 2ea63abc6e6..a6acc1e8a4d 100644
--- a/src/frontends/pytorch/src/op_table.cpp
+++ b/src/frontends/pytorch/src/op_table.cpp
@@ -92,6 +92,7 @@ OP_CONVERTER(translate_squeeze);
 OP_CONVERTER(translate_sub);
 OP_CONVERTER(translate_sum);
 OP_CONVERTER(translate_to);
+OP_CONVERTER(translate_topk);
 OP_CONVERTER(translate_transpose);
 OP_CONVERTER(translate_tril);
 OP_CONVERTER(translate_triu);
@@ -269,6 +270,7 @@ const std::map<std::string, CreatorFunction> get_supported_ops() {
         {"aten::tanh_", op::inplace_op<op::translate_1to1_match_1_inputs<opset10::Tanh>>},
         {"aten::tensor", op::translate_as_tensor},
         {"aten::to", op::translate_to},
+        {"aten::topk", op::translate_topk},
         {"aten::transpose", op::translate_transpose},
         {"aten::tril", op::translate_tril},
         {"aten::triu", op::translate_triu},
diff --git a/tests/layer_tests/pytorch_tests/test_topk.py b/tests/layer_tests/pytorch_tests/test_topk.py
new file mode 100644
index 00000000000..b2f27acdbb8
--- /dev/null
+++ b/tests/layer_tests/pytorch_tests/test_topk.py
@@ -0,0 +1,65 @@
+# Copyright (C) 2018-2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np
+import pytest
+
+from pytorch_layer_test_class import PytorchLayerTest
+
+
+class TestTopK(PytorchLayerTest):
+    def _prepare_input(self):
+        return (self.input_tensor,)
+
+    def create_model(self, k, dim, largest, sort):
+        import torch
+
+        class aten_topk(torch.nn.Module):
+            def __init__(self, k, dim, largest, sort):
+                super(aten_topk, self).__init__()
+                self.k = k
+                self.dim = dim
+                self.largest = largest
+                self.sort = sort
+
+            def forward(self, input_tensor):
+                if self.dim is None:
+                    return torch.topk(input_tensor, k=self.k, largest=self.largest, sorted=self.sort)
+                else:
+                    return torch.topk(input_tensor, k=self.k, dim=self.dim, largest=self.largest, sorted=self.sort)
+        ref_net = None
+
+        return aten_topk(k, dim, largest, sort), ref_net, "aten::topk"
+
+    @pytest.mark.parametrize(("input_tensor"), [
+        np.random.rand(7, 5, 5, 4),
+        np.random.rand(5, 6, 6, 7, 8),
+    ])
+
+    @pytest.mark.parametrize(("k"), [
+        3,
+        1,
+        2,
+    ])
+
+    @pytest.mark.parametrize(("dim"), [
+        0,
+        2,
+        -1,
+        None,
+    ])
+
+    @pytest.mark.parametrize(("largest"), [
+        True,
+        False,
+    ])
+    # For False it is hard to test because in Pytorch implementation
+    # there is not promise on the order of output values
+    @pytest.mark.parametrize(("sort"), [
+        True,
+    ])
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    def test_topK(self, input_tensor, k, dim, largest, sort, ie_device, precision, ir_version):
+        self.input_tensor = input_tensor
+        self._test(*self.create_model(k, dim, largest, sort), ie_device, precision, ir_version)