diff --git a/src/plugins/intel_gpu/src/graph/impls/onednn/convolution_onednn.hpp b/src/plugins/intel_gpu/src/graph/impls/onednn/convolution_onednn.hpp
index 74a914f56aa582..b738e3d7d7980c 100644
--- a/src/plugins/intel_gpu/src/graph/impls/onednn/convolution_onednn.hpp
+++ b/src/plugins/intel_gpu/src/graph/impls/onednn/convolution_onednn.hpp
@@ -87,11 +87,10 @@ struct ConvolutionImplementationManager : public ImplementationManager {
             return false;
 
         bool f16_conv = everyone_is(data_types::f16, in_dt, wei_dt) && one_of(out_dt, {data_types::f16, data_types::f32, data_types::u8, data_types::i8});
-        bool u8s8_conv = one_of(in_dt, {data_types::i8, data_types::u8}) &&
-                         wei_dt == data_types::i8 &&
+        bool int8_conv = one_of(in_dt, {data_types::i8, data_types::u8}) && one_of(wei_dt, {data_types::i8, data_types::u8}) &&
                          one_of(out_dt, {data_types::i32, data_types::f16, data_types::f32, data_types::u8, data_types::i8});
 
-        if (!f16_conv && !u8s8_conv)
+        if (!f16_conv && !int8_conv)
             return false;
 
         if (!is_supported_post_ops(conv_node))
diff --git a/src/plugins/intel_gpu/tests/unit/test_cases/convolution_gpu_test.cpp b/src/plugins/intel_gpu/tests/unit/test_cases/convolution_gpu_test.cpp
index 04c0cf7b79fc0a..b3ca3f7b6e9a07 100644
--- a/src/plugins/intel_gpu/tests/unit/test_cases/convolution_gpu_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/test_cases/convolution_gpu_test.cpp
@@ -4404,6 +4404,73 @@ TEST(convolution_int8_fw_gpu, quantized_convolution_u8s8f32_symmetric) {
         }
 }
 
+TEST(convolution_int8_fw_gpu, quantized_convolution_u8u8f32_symmetric) {
+    auto& engine = get_test_engine();
+
+    auto input = engine.allocate_memory({ data_types::u8, format::bfyx, { 1, 1, 5, 4 } });
+    auto weights = engine.allocate_memory({ data_types::u8, format::bfyx, { 2, 1, 3, 3 } });
+    auto biases = engine.allocate_memory({ data_types::f32, format::bfyx, { 1, 2, 1, 1 } });
+
+    set_values<uint8_t>(input, { 1, 2, 3, 4, 5,
+                                 2, 2, 3, 4, 6,
+                                 3, 3, 3, 5, 1,
+                                 1, 1, 1, 1, 1 });
+    set_values<uint8_t>(weights, { 1, 2, 1,
+                                   2, 1, 2,
+                                   9, 7, 1,
+
+                                   9, 0, 4,
+                                   1, 3, 2,
+                                   0, 2, 5 });
+    set_values(biases, { -1.0f, -8.0f });
+
+    VVVF<float> output_vec = {
+        {
+            { 70.0f, 100.0f, 25.0f },
+            { 16.0f, 18.0f, 2.0f }
+        },
+        {
+            { 48.0f, 81.0f, 43.0f },
+            { 37.0f, 29.0f, 2.0f }
+        } };
+
+    topology topology(
+        input_layout("input", input->get_layout()),
+        data("weights", weights),
+        data("biases", biases),
+        convolution("conv", input_info("input"), "weights", "biases", 1, { 2, 2 }, { 1, 1 }, { 0, 0 }, { 1, 2 }, false),
+        reorder("out", input_info("conv"), format::bfyx, data_types::f32));
+
+    ExecutionConfig config = get_test_default_config(engine);
+    config.set_property(ov::intel_gpu::optimize_data(true));
+    network network(engine, topology, config);
+    network.set_input_data("input", input);
+
+    auto outputs = network.execute();
+    ASSERT_EQ(outputs.begin()->first, "out");
+
+    auto output_memory = outputs.at("out").get_memory();
+    cldnn::mem_lock<float> output_ptr(output_memory, get_test_stream());
+
+    auto output_layout = output_memory->get_layout();
+    int y_size = output_layout.spatial(1);
+    int x_size = output_layout.spatial(0);
+    int f_size = output_layout.feature();
+    int b_size = output_layout.batch();
+    ASSERT_EQ(output_layout.format, format::bfyx);
+    ASSERT_EQ(y_size, 2);
+    ASSERT_EQ(x_size, 3);
+    ASSERT_EQ(f_size, 2);
+    ASSERT_EQ(b_size, 1);
+    for (int f = 0; f < f_size; f++)
+        for (int y = 0; y < y_size; ++y) {
+            for (int x = 0; x < x_size; ++x) {
+                ASSERT_NEAR(output_vec[f][y][x], ((float)output_ptr[f * y_size * x_size + y * x_size + x]), 1e-5f) <<
+                " x="<<x << " y=" << y << " f=" << f;
+            }
+        }
+}
+
 TEST(convolution_int8_fw_gpu, quantized_convolution_u8s8f32_asymmetric_weight_and_activations) {
     auto& engine = get_test_engine();