diff --git a/src/plugins/intel_gpu/src/graph/fully_connected.cpp b/src/plugins/intel_gpu/src/graph/fully_connected.cpp
index e04541683a0c93..78ae0386c7b115 100644
--- a/src/plugins/intel_gpu/src/graph/fully_connected.cpp
+++ b/src/plugins/intel_gpu/src/graph/fully_connected.cpp
@@ -115,38 +115,32 @@ layout fully_connected_inst::calc_output_layout(fully_connected_node const& node
     };
 
     int64_t feature = input_pshape[std::min(desc->input_size, static_cast<size_t>(4)) - 1].get_length();
+
+    if (desc->input_size == 3) {
+        feature = std::max({input_layout.spatial(0), input_layout.spatial(1), input_layout.spatial(2)});
+    }
+
+    if (weights_pshape.size() != 2) {
+        weights_layout.set_partial_shape(reshape_to_2d(weights_pshape, feature));
+    }
+
     auto output_size = tensor();
 
     // If immad is supported, spatial dimensions are reshaped to 2d in order to select oneDnn impl,
     // because oneDnn doesn't support spatial dimensions for output.
     if (supports_immad) {
-        if (desc->input_size == 3) {
-            feature = std::max({input_layout.spatial(0), input_layout.spatial(1), input_layout.spatial(2)});
-        }
-
         if (desc->input_size > 3) {
             input_layout.set_partial_shape(reshape_to_2d(input_pshape, feature));
         }
-        if (weights_pshape.size() != 2) {
-            weights_layout.set_partial_shape(reshape_to_2d(weights_pshape, feature));
-        }
 
         output_size = tensor(input_layout.batch(), weights_layout.batch(), 1, 1);
         if (desc->input_size == 3) {
             output_size = tensor(input_layout.batch(), input_layout.feature(), 1, weights_layout.batch());
         }
     } else {
-        feature = input_pshape[std::min(desc->input_size, static_cast<size_t>(5)) - 1].get_length();
-        if (desc->input_size == 3) {
-            feature = std::max({input_layout.spatial(0), input_layout.spatial(1), input_layout.spatial(2)});
-        }
-
         if (desc->input_size > 5) {
             input_layout.set_partial_shape(reshape_to_2d(input_pshape, feature));
         }
-        if (weights_pshape.size() != 2) {
-            weights_layout.set_partial_shape(reshape_to_2d(weights_pshape, feature));
-        }
 
         output_size = tensor(input_layout.batch(), weights_layout.batch(), 1, 1);
         if (desc->input_size == 3) {
diff --git a/src/plugins/intel_gpu/tests/unit/test_cases/fully_connected_gpu_test.cpp b/src/plugins/intel_gpu/tests/unit/test_cases/fully_connected_gpu_test.cpp
index 5ed5af982d7861..717d3bf8f30213 100644
--- a/src/plugins/intel_gpu/tests/unit/test_cases/fully_connected_gpu_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/test_cases/fully_connected_gpu_test.cpp
@@ -461,17 +461,17 @@ TEST(fully_connected_gpu, no_biases_5d_input) {
         return;
 
     //  Input  : 1x8x8x8x12
-    //  Weights: 48x12x1x1
+    //  Weights: 48x12
     //  Output : 1x8x8x8x48
 
     const int32_t input_b = 1, input_f = 8, input_z = 8, input_y = 8, input_x = 12, // size of the whole input buffer
-                  weight_b = 48, weight_f = 12, weight_y = 1, weight_x = 1;         // size of the whole weights buffer
+                  weight_b = 48, weight_f = 12;                                     // size of the whole weights buffer
 
     auto input_prim = engine.allocate_memory({ data_types::f32, format::bfzyx, { input_b, input_f, input_x, input_y, input_z } });
-    auto weights_prim = engine.allocate_memory({ data_types::f32, format::bfyx, { weight_b, weight_f, weight_x, weight_y } });
+    auto weights_prim = engine.allocate_memory({ { weight_b, weight_f }, data_types::f32, format::bfyx });
 
     std::vector<float> input_data(input_b * input_f * input_z * input_y * input_x, 0);
-    std::vector<float> weights_data(weight_b * weight_f * weight_y * weight_x, 0);
+    std::vector<float> weights_data(weight_b * weight_f, 0);
 
     set_values(input_prim, std::move(input_data));
     set_values(weights_prim, std::move(weights_data));
@@ -501,17 +501,17 @@ TEST(fully_connected_gpu, no_biases_5d_input_immad) {
         return;
 
     //  Input  : 1x8x8x8x12
-    //  Weights: 48x12x1x1
+    //  Weights: 48x12
     //  Output : 512x48x1x1
 
     const int32_t input_b = 1, input_f = 8, input_z = 8, input_y = 8, input_x = 12, // size of the whole input buffer
-                  weight_b = 48, weight_f = 12, weight_y = 1, weight_x = 1;         // size of the whole weights buffer
+                  weight_b = 48, weight_f = 12;                                     // size of the whole weights buffer
 
     auto input_prim = engine.allocate_memory({ data_types::f32, format::bfzyx, { input_b, input_f, input_x, input_y, input_z } });
-    auto weights_prim = engine.allocate_memory({ data_types::f32, format::bfyx, { weight_b, weight_f, weight_x, weight_y } });
+    auto weights_prim = engine.allocate_memory({ { weight_b, weight_f }, data_types::f32, format::bfyx });
 
     std::vector<float> input_data(input_b * input_f * input_z * input_y * input_x, 0);
-    std::vector<float> weights_data(weight_b * weight_f * weight_y * weight_x, 0);
+    std::vector<float> weights_data(weight_b * weight_f, 0);
 
     set_values(input_prim, std::move(input_data));
     set_values(weights_prim, std::move(weights_data));
@@ -536,8 +536,8 @@ TEST(fully_connected_gpu, no_biases_5d_input_immad) {
     auto outputs = network.execute();
     ASSERT_EQ(outputs.begin()->second.get_layout().batch(), input_f*input_z*input_y);
     ASSERT_EQ(outputs.begin()->second.get_layout().feature(), weight_b);
-    ASSERT_EQ(outputs.begin()->second.get_layout().spatial(1), weight_y);
-    ASSERT_EQ(outputs.begin()->second.get_layout().spatial(0), weight_x);
+    ASSERT_EQ(outputs.begin()->second.get_layout().spatial(1), 1);
+    ASSERT_EQ(outputs.begin()->second.get_layout().spatial(0), 1);
 }
 
 TEST(fully_connected_gpu, xb_f32_batch_1) {