diff --git a/src/frontends/pytorch/src/op/quantile.cpp b/src/frontends/pytorch/src/op/quantile.cpp
new file mode 100644
index 00000000000000..2eefb34bbca029
--- /dev/null
+++ b/src/frontends/pytorch/src/op/quantile.cpp
@@ -0,0 +1,80 @@
+// Copyright (C) 2018-2025 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "openvino/frontend/pytorch/node_context.hpp"
+#include "openvino/op/convert.hpp"
+#include "openvino/op/gather.hpp"
+#include "openvino/op/range.hpp"
+#include "openvino/op/reshape.hpp"
+#include "openvino/opsets/opset10.hpp"
+#include "openvino/op/multiply.hpp"
+#include "openvino/op/floor.hpp"
+#include "openvino/op/add.hpp"
+#include "openvino/op/subtract.hpp"
+#include "openvino/op/maximum.hpp"
+#include "openvino/op/minimum.hpp"
+#include "utils.hpp"
+
+using namespace ov::op;
+
+OutputVector translate_quantile(const NodeContext& context) {
+    num_inputs_check(context, 2, 5);
+
+    auto input = context.get_input(0);
+    auto q = context.get_input(1);      // Quantile(s), can be float or tensor
+
+    auto dim = context.input_is_none(2) ? -1 : context.get_input<int64_t>(2);
+    auto keepdim = context.input_is_none(3) ? false : context.get_input<bool>(3);
+    auto interpolation = context.input_is_none(4) ? "linear" : context.get_input<std::string>(4);
+
+
+    if (dim == -1) {
+        input = context.mark_node(std::make_shared<v0::Reshape>(
+            input, context.mark_node(std::make_shared<v0::Range>(0, input.get_shape().size(), 1)), true));
+        dim = 0;
+    }
+
+    auto sorted = context.mark_node(std::make_shared<v0::Sort>(input, dim, true)); // Ascending order
+
+    auto dim_size = input.get_shape()[dim];
+
+    auto indices = context.mark_node(std::make_shared<v0::Multiply>(q, dim_size - 1));
+    auto lower_indices = context.mark_node(std::make_shared<v0::Floor>(indices));
+    auto upper_indices = context.mark_node(std::make_shared<v1::Add>(lower_indices, 1));
+    auto weights = context.mark_node(std::make_shared<v1::Subtract>(indices, lower_indices));
+    auto lower_values = context.mark_node(std::make_shared<v1::Gather>(sorted, lower_indices, dim));
+    auto upper_values = context.mark_node(std::make_shared<v1::Gather>(sorted, upper_indices, dim));
+
+    Output<Node> result;
+    if (interpolation == "linear") {
+        result = context.mark_node(std::make_shared<v1::Add>(
+            lower_values, context.mark_node(std::make_shared<v1::Multiply>(weights, upper_values))));
+    } else if (interpolation == "lower") {
+        result = lower_values;
+    } else if (interpolation == "higher") {
+        result = upper_values;
+    } else if (interpolation == "nearest") {
+        auto nearest_indices = context.mark_node(std::make_shared<v0::Round>(indices));
+        result = context.mark_node(std::make_shared<v1::Gather>(sorted, nearest_indices, dim));
+    } else if (interpolation == "midpoint") {
+        result = context.mark_node(std::make_shared<v1::Add>(
+            lower_values, context.mark_node(std::make_shared<v1::Multiply>(
+                              context.mark_node(std::make_shared<v0::Constant>(element::f32, Shape{}, 0.5)),
+                              context.mark_node(std::make_shared<v1::Subtract>(upper_values, lower_values))))));
+    } else {
+        throw std::runtime_error("Unsupported interpolation method: " + interpolation);
+    }
+    if (!keepdim) {
+        auto reshape_dims = input.get_shape();
+        reshape_dims.erase(reshape_dims.begin() + dim);
+        result = context.mark_node(std::make_shared<v0::Reshape>(result, reshape_dims, true));
+    }
+
+    return {result};
+}
+
+}  // 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 ef75a253f7506a..a1e05bd0dfb799 100644
--- a/src/frontends/pytorch/src/op_table.cpp
+++ b/src/frontends/pytorch/src/op_table.cpp
@@ -191,6 +191,7 @@ OP_CONVERTER(translate_quantized_add);
 OP_CONVERTER(translate_quantized_add_relu);
 OP_CONVERTER(translate_quantized_hardswish);
 OP_CONVERTER(translate_quantized_mul);
+OP_CONVERTER(translate_quantile);
 OP_CONVERTER(translate_range_length);
 OP_CONVERTER(translate_rand);
 OP_CONVERTER(translate_randn);
@@ -747,6 +748,7 @@ const std::unordered_map<std::string, CreatorFunction> get_supported_ops_ts() {
         {"quantized::hardswish", op::translate_quantized_hardswish},
         {"quantized::linear", op::translate_quantized_linear},
         {"quantized::mul", op::translate_quantized_mul},
+        {"quantized::relu", op::translate_quantile},
         {"torchvision::deform_conv2d", op::translate_deform_conv},
         {"torchvision::nms", op::translate_nms},
         {"torchvision::roi_align", op::translate_roi_align},
diff --git a/tests/layer_tests/pytorch_tests/test_quantile.py b/tests/layer_tests/pytorch_tests/test_quantile.py
new file mode 100644
index 00000000000000..4dff96d4c96de2
--- /dev/null
+++ b/tests/layer_tests/pytorch_tests/test_quantile.py
@@ -0,0 +1,36 @@
+# Copyright (C) 2018-2025 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import pytest
+import numpy as np
+import torch
+from pytorch_layer_test_class import PytorchLayerTest
+
+
+class TestQuantile(PytorchLayerTest):
+    def _prepare_input(self):
+        input_tensor = np.random.randn(1, 3, 224, 224).astype(np.float32)
+        quantile = np.array(0.5, dtype=np.float32)
+        return (input_tensor, quantile)
+
+    def create_model(self, dim=None, keepdim=False):
+        class aten_quantile(torch.nn.Module):
+            def __init__(self, dim, keepdim):
+                super(aten_quantile, self).__init__()
+                self.dim = dim
+                self.keepdim = keepdim
+
+            def forward(self, x, q):
+                return torch.quantile(x, q, dim=self.dim, keepdim=self.keepdim)
+
+        ref_net = None
+
+        return aten_quantile(dim, keepdim), ref_net, "aten::quantile"
+
+    @pytest.mark.parametrize("dim", [None, 0, 1, 2, 3, -1, -2, -3])
+    @pytest.mark.parametrize("keepdim", [True, False])
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    def test_quantile(self, dim, keepdim, ie_device, precision, ir_version):
+        self._test(*self.create_model(dim, keepdim), ie_device, precision, ir_version)
+