diff --git a/src/frontends/pytorch/src/op/roll.cpp b/src/frontends/pytorch/src/op/roll.cpp
new file mode 100644
index 00000000000000..39b9a37e9f217e
--- /dev/null
+++ b/src/frontends/pytorch/src/op/roll.cpp
@@ -0,0 +1,37 @@
+// Copyright (C) 2018-2022 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "openvino/frontend/pytorch/node_context.hpp"
+#include "openvino/opsets/opset8.hpp"
+#include "utils.hpp"
+
+namespace ov {
+namespace frontend {
+namespace pytorch {
+namespace op {
+
+OutputVector translate_roll(NodeContext& context) {
+    const auto data = context.get_input(0);
+    const auto shifts = context.get_input(1);
+    const auto axes = context.get_input(2);
+    const auto shifts_pshape = shifts.get_partial_shape();
+    const auto axes_pshape = axes.get_partial_shape();
+    const auto match_dims = axes_pshape.compatible(shifts_pshape);
+    if (!match_dims) {
+        const auto const_minus_1 = opset8::Constant::create(element::i32, Shape{1}, {-1});
+        const auto axis_0 = opset8::Constant::create(element::i32, Shape{1}, {0});
+        const auto flat = std::make_shared<opset8::Reshape>(data, const_minus_1, false);
+        const auto roll = std::make_shared<opset8::Roll>(flat, shifts, axis_0);
+        const auto shape_of_data = std::make_shared<opset8::ShapeOf>(data);
+        const auto reshape = std::make_shared<opset8::Reshape>(roll, shape_of_data, false);
+        context.mark_nodes({const_minus_1, flat, roll, shape_of_data, reshape});
+        return {reshape};
+    }
+    return {context.mark_node(std::make_shared<opset8::Roll>(data, shifts, axes))};
+};
+
+}  // 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 4d16b18ee8744a..2ceeb928cd56e1 100644
--- a/src/frontends/pytorch/src/op_table.cpp
+++ b/src/frontends/pytorch/src/op_table.cpp
@@ -59,6 +59,7 @@ OP_CONVERTER(translate_relu6);
 OP_CONVERTER(translate_reshape);
 OP_CONVERTER(translate_reshape_as);
 OP_CONVERTER(translate_rsub);
+OP_CONVERTER(translate_roll);
 OP_CONVERTER(translate_rsqrt);
 OP_CONVERTER(translate_select);
 OP_CONVERTER(translate_size);
@@ -155,6 +156,7 @@ const std::map<std::string, CreatorFunction> get_supported_ops() {
         {"aten::reshape", op::translate_reshape},
         {"aten::reshape_as", op::translate_reshape_as},
         {"aten::rsub", op::translate_rsub},
+        {"aten::roll", op::translate_roll},
         {"aten::rsqrt", op::translate_rsqrt},
         {"aten::select", op::translate_select},
         {"aten::sigmoid", op::translate_1to1_match_1_inputs<opset8::Sigmoid>},
diff --git a/tests/layer_tests/pytorch_tests/test_roll.py b/tests/layer_tests/pytorch_tests/test_roll.py
new file mode 100644
index 00000000000000..0acc914bdd6618
--- /dev/null
+++ b/tests/layer_tests/pytorch_tests/test_roll.py
@@ -0,0 +1,41 @@
+# Copyright (C) 2018-2022 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import pytest
+import numpy as np
+from pytorch_layer_test_class import PytorchLayerTest
+
+
+class TestRoll(PytorchLayerTest):
+    def _prepare_input(self):
+        return (np.random.uniform(0, 50, (2, 3, 4)).astype(np.float32),)
+
+    def create_model(self, shifts, dim):
+
+        import torch
+
+        class aten_roll(torch.nn.Module):
+            def __init__(self, shifts, dim=None):
+                super(aten_roll, self).__init__()
+                self.dim = dim 
+                self.shits = shifts
+
+            def forward(self, x):
+                if self.dim is not None:
+                    return torch.roll(x, self.shits, self.dim)
+                return torch.roll(x, self.shits)
+
+        ref_net = None
+
+        return aten_roll(shifts, dim), ref_net, "aten::roll"
+
+    @pytest.mark.parametrize(("shifts", "dim"), [
+            [(2, 1), (0, 1)],
+            [1, 0],
+            [-1, 0],
+            [1, None],
+    ])
+    @pytest.mark.nightly
+    def test_roll(self, shifts, dim, ie_device, precision, ir_version):
+        self._test(*self.create_model(shifts, dim), ie_device, precision, ir_version)
+