diff --git a/src/plugins/intel_gpu/src/plugin/transformations/lora_horizontal_fusion.cpp b/src/plugins/intel_gpu/src/plugin/transformations/lora_horizontal_fusion.cpp
index f0acdc403abc32..d2a433ce46e339 100644
--- a/src/plugins/intel_gpu/src/plugin/transformations/lora_horizontal_fusion.cpp
+++ b/src/plugins/intel_gpu/src/plugin/transformations/lora_horizontal_fusion.cpp
@@ -106,46 +106,19 @@ LoRAHorizontalFusion::LoRAHorizontalFusion() {
             old_multiply->clear_control_dependencies();
         }
 
-        bool fuse_second_matmul = true;
-        size_t not_concatenable_idx = 0;
-        const auto& base_dim = variable_b_nodes[0]->get_output_partial_shape(0)[not_concatenable_idx];
-        for (size_t i = 1; i < variable_b_nodes.size(); ++i) {
-            const auto& dim = variable_b_nodes[i]->get_output_partial_shape(0)[not_concatenable_idx];
-            if (dim.is_dynamic() || dim.get_length() != base_dim.get_length()) {
-                fuse_second_matmul = false;
-            }
+        auto axis_const = ov::op::v0::Constant::create(ov::element::i64, ov::Shape{}, {fused_multiply->get_output_partial_shape(0).size() - 1});
+        auto output_split = std::make_shared<ov::op::v1::Split>(fused_multiply, axis_const, matmul2_nodes.size());
+        auto split_name = fused_multiply->get_friendly_name() + "_split";
+        copy_runtime_info(fused_multiply, output_split);
+        output_split->set_friendly_name(split_name);
+        for (size_t i = 0; i < matmul2_nodes.size(); ++i) {
+            matmul2_nodes[i]->input(0).replace_source_output(output_split->output(i));
         }
 
-        std::shared_ptr<ov::Node> fused_matmul2 = nullptr;
-        if (fuse_second_matmul) {
-            auto fused_variable_b = std::make_shared<ov::op::v0::Concat>(variable_b_nodes, 1);
-            fused_variable_b->set_friendly_name(variable_b_nodes[0]->get_friendly_name() +
-                                                "_fused" + std::to_string(variable_b_nodes.size()) + "_ReadValues");
-            ov::copy_runtime_info(variable_b_nodes, fused_variable_b);
-
-            bool transpose_a2 = std::dynamic_pointer_cast<ov::op::v0::MatMul>(matmul2_nodes[0])->get_transpose_a();
-            bool transpose_b2 = std::dynamic_pointer_cast<ov::op::v0::MatMul>(matmul2_nodes[0])->get_transpose_b();
-            fused_matmul2 = std::make_shared<ov::op::v0::MatMul>(fused_multiply, fused_variable_b, transpose_a2, transpose_b2);
-            auto matmul2_name = matmul2_nodes[0]->get_friendly_name() + "_fused_" + std::to_string(matmul2_nodes.size()) + "_MatMuls";
-            fused_matmul2->set_friendly_name(matmul2_name);
-            ov::copy_runtime_info(matmul2_nodes, fused_matmul2);
-            for (const auto& old_matmul2 : matmul2_nodes) {
-                old_matmul2->clear_control_dependencies();
-            }
-        } else {
-            auto axis_const = ov::op::v0::Constant::create(ov::element::i64, ov::Shape{}, {fused_multiply->get_output_partial_shape(0).size() - 1});
-            auto output_split = std::make_shared<ov::op::v1::Split>(fused_multiply, axis_const, matmul2_nodes.size());
-            auto split_name = fused_multiply->get_friendly_name() + "_split";
-            copy_runtime_info(fused_multiply, output_split);
-            output_split->set_friendly_name(split_name);
-            for (size_t i = 0; i < matmul2_nodes.size(); ++i) {
-                matmul2_nodes[i]->input(0).replace_source_output(output_split->output(i));
-            }
-
-            fused_matmul2 = std::make_shared<ov::op::v0::Concat>(matmul2_nodes, matmul2_nodes[0]->get_output_partial_shape(0).size() - 1);
-            auto matmul2_name = matmul2_nodes[0]->get_friendly_name() + "_fused_" + std::to_string(matmul2_nodes.size()) + "_MatMuls_output";
-            fused_matmul2->set_friendly_name(matmul2_name);
-        }
+        auto fused_matmul2 = std::make_shared<ov::op::v0::Concat>(matmul2_nodes, matmul2_nodes[0]->get_output_partial_shape(0).size() - 1);
+        auto matmul2_name = matmul2_nodes[0]->get_friendly_name() + "_fused_" + std::to_string(matmul2_nodes.size()) + "_MatMuls_output";
+        fused_matmul2->set_friendly_name(matmul2_name);
+        ov::copy_runtime_info(matmul2_nodes, fused_matmul2);
 
         auto fused_add = std::make_shared<ov::op::v1::Add>(split->get_input_node_shared_ptr(0), fused_matmul2);
         auto fused_add_name = add_nodes[0]->get_friendly_name() + "_fused_" + std::to_string(add_nodes.size()) + "_Adds";
diff --git a/src/plugins/intel_gpu/tests/unit/transformations/lora_horizontal_fusion.cpp b/src/plugins/intel_gpu/tests/unit/transformations/lora_horizontal_fusion.cpp
new file mode 100644
index 00000000000000..67fcd1bba497ae
--- /dev/null
+++ b/src/plugins/intel_gpu/tests/unit/transformations/lora_horizontal_fusion.cpp
@@ -0,0 +1,171 @@
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include "common_test_utils/ov_test_utils.hpp"
+#include "openvino/opsets/opset6.hpp"
+
+#include "plugin/transformations/lora_horizontal_fusion.hpp"
+#include "intel_gpu/op/placeholder.hpp"
+#include "intel_gpu/op/fully_connected_compressed.hpp"
+
+using namespace testing;
+using namespace ov::intel_gpu;
+
+namespace ov {
+namespace test {
+namespace intel_gpu {
+
+TEST_F(TransformationTestsF, LoRAHorizontalFusion) {
+    ov::element::Type model_dt = ov::element::f16;
+    {
+        auto lora_input = std::make_shared<ov::op::v0::Parameter>(model_dt, ov::PartialShape{-1, -1, 2048});
+        auto weights = std::make_shared<ov::op::v0::Constant>(ov::element::u8, ov::Shape{2560, 2048});
+        auto bias = std::make_shared<ov::intel_gpu::op::Placeholder>();
+        auto scale = std::make_shared<ov::op::v0::Constant>(model_dt, ov::Shape{2560, 1});
+        auto fc_fused = std::make_shared<ov::intel_gpu::op::FullyConnectedCompressed>(lora_input, weights, bias, scale);
+
+        auto axis_const = ov::op::v0::Constant::create(ov::element::i64, ov::Shape{1}, {2});
+        auto split_const = ov::op::v0::Constant::create(ov::element::i64, ov::Shape{3}, {2048, 256, 256});
+        auto split = std::make_shared<ov::op::v1::VariadicSplit>(fc_fused, axis_const, split_const);
+
+        auto variable_a_0 = std::make_shared<ov::op::util::Variable>(
+            ov::op::util::VariableInfo{ov::PartialShape({-1, 2048}), model_dt, "var_a_0"});
+        auto variable_alpha_0 = std::make_shared<ov::op::util::Variable>(
+            ov::op::util::VariableInfo{ov::PartialShape({1, -1}), model_dt, "var_alpha_0"});
+        auto variable_b_0 = std::make_shared<ov::op::util::Variable>(
+            ov::op::util::VariableInfo{ov::PartialShape({2048, -1}), model_dt, "var_b_0"});
+        auto read_value_a_0 = std::make_shared<ov::op::v6::ReadValue>(variable_a_0);
+        auto read_value_alpha_0 = std::make_shared<ov::op::v6::ReadValue>(variable_alpha_0);
+        auto read_value_b_0 = std::make_shared<ov::op::v6::ReadValue>(variable_b_0);
+        auto matmul1_0 = std::make_shared<ov::op::v0::MatMul>(lora_input, read_value_a_0, false, true);
+        auto multiply_0 = std::make_shared<ov::op::v1::Multiply>(matmul1_0, read_value_alpha_0);
+        auto matmul2_0 = std::make_shared<ov::op::v0::MatMul>(multiply_0, read_value_b_0, false, true);
+        auto add_0 = std::make_shared<ov::op::v1::Add>(split->output(0), matmul2_0);
+
+        auto variable_a_1 = std::make_shared<ov::op::util::Variable>(
+            ov::op::util::VariableInfo{ov::PartialShape({-1, 2048}), model_dt, "var_a_1"});
+        auto variable_alpha_1 = std::make_shared<ov::op::util::Variable>(
+            ov::op::util::VariableInfo{ov::PartialShape({1, -1}), model_dt, "var_alpha_1"});
+        auto variable_b_1 = std::make_shared<ov::op::util::Variable>(
+            ov::op::util::VariableInfo{ov::PartialShape({256, -1}), model_dt, "var_b_1"});
+        auto read_value_a_1 = std::make_shared<ov::op::v6::ReadValue>(variable_a_1);
+        auto read_value_alpha_1 = std::make_shared<ov::op::v6::ReadValue>(variable_alpha_1);
+        auto read_value_b_1 = std::make_shared<ov::op::v6::ReadValue>(variable_b_1);
+        auto matmul1_1 = std::make_shared<ov::op::v0::MatMul>(lora_input, read_value_a_1, false, true);
+        auto multiply_1 = std::make_shared<ov::op::v1::Multiply>(matmul1_1, read_value_alpha_1);
+        auto matmul2_1 = std::make_shared<ov::op::v0::MatMul>(multiply_1, read_value_b_1, false, true);
+        auto add_1 = std::make_shared<ov::op::v1::Add>(split->output(1), matmul2_1);
+
+       auto variable_a_2 = std::make_shared<ov::op::util::Variable>(
+            ov::op::util::VariableInfo{ov::PartialShape({-1, 2048}), model_dt, "var_a_2"});
+        auto variable_alpha_2 = std::make_shared<ov::op::util::Variable>(
+            ov::op::util::VariableInfo{ov::PartialShape({1, -1}), model_dt, "var_alpha_2"});
+        auto variable_b_2 = std::make_shared<ov::op::util::Variable>(
+            ov::op::util::VariableInfo{ov::PartialShape({256, -1}), model_dt, "var_b_2"});
+        auto read_value_a_2 = std::make_shared<ov::op::v6::ReadValue>(variable_a_2);
+        auto read_value_alpha_2 = std::make_shared<ov::op::v6::ReadValue>(variable_alpha_2);
+        auto read_value_b_2 = std::make_shared<ov::op::v6::ReadValue>(variable_b_2);
+        auto matmul1_2 = std::make_shared<ov::op::v0::MatMul>(lora_input, read_value_a_2, false, true);
+        auto multiply_2 = std::make_shared<ov::op::v1::Multiply>(matmul1_2, read_value_alpha_2);
+        auto matmul2_2 = std::make_shared<ov::op::v0::MatMul>(multiply_2, read_value_b_2, false, true);
+        auto add_2 = std::make_shared<ov::op::v1::Add>(split->output(2), matmul2_2);
+
+        auto reshape_pattern0 = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{4}, std::vector<int64_t>{0, 0, 32, 64});
+        auto reshape_pattern1 = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{4}, std::vector<int64_t>{0, 0, 4, 64});
+        auto reshape_pattern2 = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{4}, std::vector<int64_t>{0, 0, 4, 64});
+        auto reshape0 = std::make_shared<ov::op::v1::Reshape>(add_0, reshape_pattern0, true);
+        auto reshape1 = std::make_shared<ov::op::v1::Reshape>(add_1, reshape_pattern1, true);
+        auto reshape2 = std::make_shared<ov::op::v1::Reshape>(add_2, reshape_pattern2, true);
+
+        auto result0 = std::make_shared<ov::op::v0::Result>(reshape0);
+        auto result1 = std::make_shared<ov::op::v0::Result>(reshape1);
+        auto result2 = std::make_shared<ov::op::v0::Result>(reshape2);
+
+        model = std::make_shared<ov::Model>(ov::NodeVector{result0, result1, result2}, ov::ParameterVector{lora_input});
+        manager.register_pass<LoRAHorizontalFusion>();
+    }
+
+    {
+        auto lora_input = std::make_shared<ov::op::v0::Parameter>(model_dt, ov::PartialShape{-1, -1, 2048});
+        auto weights = std::make_shared<ov::op::v0::Constant>(ov::element::u8, ov::Shape{2560, 2048});
+        auto bias = std::make_shared<ov::intel_gpu::op::Placeholder>();
+        auto scale = std::make_shared<ov::op::v0::Constant>(model_dt, ov::Shape{2560, 1});
+        auto fc_fused = std::make_shared<ov::intel_gpu::op::FullyConnectedCompressed>(lora_input, weights, bias, scale);
+
+        auto variable_a_0 = std::make_shared<ov::op::util::Variable>(
+            ov::op::util::VariableInfo{ov::PartialShape({-1, 2048}), model_dt, "var_a_0"});
+        auto variable_a_1 = std::make_shared<ov::op::util::Variable>(
+            ov::op::util::VariableInfo{ov::PartialShape({-1, 2048}), model_dt, "var_a_1"});
+        auto variable_a_2 = std::make_shared<ov::op::util::Variable>(
+            ov::op::util::VariableInfo{ov::PartialShape({-1, 2048}), model_dt, "var_a_2"});
+
+        auto read_value_a_0 = std::make_shared<ov::op::v6::ReadValue>(variable_a_0);
+        auto read_value_a_1 = std::make_shared<ov::op::v6::ReadValue>(variable_a_1);
+        auto read_value_a_2 = std::make_shared<ov::op::v6::ReadValue>(variable_a_2);
+        auto concat_variable_a = std::make_shared<ov::op::v0::Concat>(NodeVector{read_value_a_0, read_value_a_1, read_value_a_2}, 0);
+
+        auto fused_matmul1 = std::make_shared<ov::op::v0::MatMul>(lora_input, concat_variable_a, false, true);
+
+        auto variable_alpha_0 = std::make_shared<ov::op::util::Variable>(
+            ov::op::util::VariableInfo{ov::PartialShape({1, -1}), model_dt, "var_alpha_0"});
+        auto variable_alpha_1 = std::make_shared<ov::op::util::Variable>(
+            ov::op::util::VariableInfo{ov::PartialShape({1, -1}), model_dt, "var_alpha_1"});
+        auto variable_alpha_2 = std::make_shared<ov::op::util::Variable>(
+            ov::op::util::VariableInfo{ov::PartialShape({1, -1}), model_dt, "var_alpha_2"});
+
+        auto read_value_alpha_0 = std::make_shared<ov::op::v6::ReadValue>(variable_alpha_0);
+        auto read_value_alpha_1 = std::make_shared<ov::op::v6::ReadValue>(variable_alpha_1);
+        auto read_value_alpha_2 = std::make_shared<ov::op::v6::ReadValue>(variable_alpha_2);
+        auto concat_variable_alpha = std::make_shared<ov::op::v0::Concat>(NodeVector{read_value_alpha_0, read_value_alpha_1, read_value_alpha_2}, 1);
+
+        auto multiply = std::make_shared<ov::op::v1::Multiply>(fused_matmul1, concat_variable_alpha);
+
+        auto split_axis = ov::op::v0::Constant::create(ov::element::i64, ov::Shape{}, {2});
+        auto split = std::make_shared<ov::op::v1::Split>(multiply, split_axis, 3);
+
+        auto variable_b_0 = std::make_shared<ov::op::util::Variable>(
+            ov::op::util::VariableInfo{ov::PartialShape({2048, -1}), model_dt, "var_b_0"});
+        auto variable_b_1 = std::make_shared<ov::op::util::Variable>(
+            ov::op::util::VariableInfo{ov::PartialShape({256, -1}), model_dt, "var_b_1"});
+        auto variable_b_2 = std::make_shared<ov::op::util::Variable>(
+            ov::op::util::VariableInfo{ov::PartialShape({256, -1}), model_dt, "var_b_2"});
+
+        auto read_value_b_0 = std::make_shared<ov::op::v6::ReadValue>(variable_b_0);
+        auto read_value_b_1 = std::make_shared<ov::op::v6::ReadValue>(variable_b_1);
+        auto read_value_b_2 = std::make_shared<ov::op::v6::ReadValue>(variable_b_2);
+
+        auto matmul2_0 = std::make_shared<ov::op::v0::MatMul>(split->output(0), read_value_b_0, false, true);
+        auto matmul2_1 = std::make_shared<ov::op::v0::MatMul>(split->output(1), read_value_b_1, false, true);
+        auto matmul2_2 = std::make_shared<ov::op::v0::MatMul>(split->output(2), read_value_b_2, false, true);
+
+        auto concat_matmul2 = std::make_shared<ov::op::v0::Concat>(NodeVector{matmul2_0, matmul2_1, matmul2_2}, 2);
+
+        auto add = std::make_shared<ov::op::v1::Add>(fc_fused, concat_matmul2);
+
+        auto axis_const = ov::op::v0::Constant::create(ov::element::i64, ov::Shape{1}, {2});
+        auto split_const = ov::op::v0::Constant::create(ov::element::i64, ov::Shape{3}, {2048, 256, 256});
+        auto var_split = std::make_shared<ov::op::v1::VariadicSplit>(add, axis_const, split_const);
+
+        auto reshape_pattern0 = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{4}, std::vector<int64_t>{0, 0, 32, 64});
+        auto reshape_pattern1 = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{4}, std::vector<int64_t>{0, 0, 4, 64});
+        auto reshape_pattern2 = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{4}, std::vector<int64_t>{0, 0, 4, 64});
+        auto reshape0 = std::make_shared<ov::op::v1::Reshape>(var_split->output(0), reshape_pattern0, true);
+        auto reshape1 = std::make_shared<ov::op::v1::Reshape>(var_split->output(1), reshape_pattern1, true);
+        auto reshape2 = std::make_shared<ov::op::v1::Reshape>(var_split->output(2), reshape_pattern2, true);
+
+        auto result0 = std::make_shared<ov::op::v0::Result>(reshape0);
+        auto result1 = std::make_shared<ov::op::v0::Result>(reshape1);
+        auto result2 = std::make_shared<ov::op::v0::Result>(reshape2);
+
+        model_ref = std::make_shared<ov::Model>(ov::NodeVector{result0, result1, result2}, ov::ParameterVector{lora_input});
+        comparator.enable(FunctionsComparator::ATTRIBUTES);
+        comparator.enable(FunctionsComparator::CmpValues::ACCURACY);
+    }
+}
+
+}  // namespace intel_gpu
+}  // namespace test
+}  // namespace ov