diff --git a/src/plugins/intel_gpu/src/graph/graph_optimizer/prepare_buffer_fusing.cpp b/src/plugins/intel_gpu/src/graph/graph_optimizer/prepare_buffer_fusing.cpp
index e49a2b909225e5..51ac026c7c5060 100644
--- a/src/plugins/intel_gpu/src/graph/graph_optimizer/prepare_buffer_fusing.cpp
+++ b/src/plugins/intel_gpu/src/graph/graph_optimizer/prepare_buffer_fusing.cpp
@@ -699,8 +699,10 @@ void crop_in_place_optimization::update_in_place_crop_padding_simple_data_format
             auto reshape_axis = crop_axis;
             if (reshape_mode == reshape::reshape_mode::base) {
                 if (crop_axis == 0 && !crop_layout.get_partial_shape()[0].is_dynamic() &&
-                    crop_layout.get_partial_shape()[0].get_length() == 1) {
-                    // The crop produces exactly batch=1 per slice.
+                    crop_layout.get_partial_shape()[0].get_length() == 1 &&
+                    !(user_info.second.get_partial_shape()[0].is_static() &&
+                      user_info.second.get_partial_shape()[0].get_length() == 1)) {
+                    // The crop produces exactly batch=1 per slice and the reshape squeezes that dim.
                     // The reshape absorbs that dim, so the padding axis in the output remains 0.
                     reshape_axis = 0;
                 } else {
@@ -764,9 +766,10 @@ void crop_in_place_optimization::update_in_place_crop_padding_simple_data_format
                 std::vector<ov::Dimension::value_type> reshape_upper_sizes(output_rank, 0);
                 padding::DynamicDimsMask reshape_dyn_pad_mask;
 
-                if (crop_axis == 0 && crop_dim_val == 1) {
-                    // The crop splits on the batch axis with exactly batch=1 per slice.
-                    // The reshape squeezes that batch=1 dim: [1, f, y, x] -> [f, y, x].
+                if (crop_axis == 0 && crop_dim_val == 1 &&
+                    !(reshape_ps[0].is_static() && reshape_ps[0].get_length() == 1)) {
+                    // The crop splits on the batch axis with exactly batch=1 per slice
+                    // and the reshape squeezes that batch=1 dim: [1, f, y, x] -> [f, y, x].
                     // Padding offsets are in units of one 4D batch slice (= f*y*x elements),
                     // but the 3D output counts elements at axis 0 directly, so multiply by f.
                     const auto batch_stride_factor = reshape_ps[0].get_length();
diff --git a/src/plugins/intel_gpu/src/graph/include/reshape_inst.h b/src/plugins/intel_gpu/src/graph/include/reshape_inst.h
index dbeb07ccedfd71..36a5ffcf72960a 100644
--- a/src/plugins/intel_gpu/src/graph/include/reshape_inst.h
+++ b/src/plugins/intel_gpu/src/graph/include/reshape_inst.h
@@ -72,7 +72,14 @@ struct typed_program_node<reshape> : public typed_program_node_base<reshape> {
         if (axis == 0 && !input_pshape[0].is_dynamic()) {
             if (prim->output_pattern.empty())
                 return false;
-            return input_pshape[0].get_length() == 1;
+            if (input_pshape[0].get_length() != 1)
+                return false;
+            // Reject if the reshape just flattens spatial dims while keeping batch=1
+            // (e.g. [1,C,H,W] -> [1,C,H*W]).  Only allow when the batch dim is truly squeezed.
+            auto& out_ps = prim->output_partial_shape;
+            if (!out_ps[0].is_dynamic() && out_ps[0].get_length() == 1)
+                return false;
+            return true;
         }
 
         auto input_rank = input_pshape.size();
diff --git a/src/plugins/intel_gpu/tests/unit/passes/prepare_buffer_fusing_test.cpp b/src/plugins/intel_gpu/tests/unit/passes/prepare_buffer_fusing_test.cpp
index bfdc024bb778ae..c48040eb8bc329 100644
--- a/src/plugins/intel_gpu/tests/unit/passes/prepare_buffer_fusing_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/passes/prepare_buffer_fusing_test.cpp
@@ -1842,3 +1842,72 @@ TEST(prepare_buffer_fusing, in_place_crop_dynamic_batch_axis_split_with_reshape)
     for (size_t i = 0; i < slice_elems; i++)
         ASSERT_FLOAT_EQ(v_out[i], input_data[2 * slice_elems + i]) << "V mismatch at " << i;
 }
+
+// RAFT-like pattern: VariadicSplit on batch axis → crop [1,C,H,W] → Reshape [1,C,H*W]
+// The reshape flattens spatial dims while keeping batch=1.  The in-place crop
+// optimisation must NOT treat this as a batch-squeeze and must produce correct data.
+TEST(prepare_buffer_fusing, in_place_crop_dynamic_batch_axis_split_with_spatial_flatten_reshape) {
+    auto& engine = get_test_engine();
+    tests::random_generator rg(GET_SUITE_NAME);
+
+    const size_t dim_b = 2, dim_c = 4, dim_h = 3, dim_w = 5;
+    const size_t slice_elems = dim_c * dim_h * dim_w;
+
+    auto in_layout = layout{ov::PartialShape{static_cast<int64_t>(dim_b), -1, static_cast<int64_t>(dim_h), static_cast<int64_t>(dim_w)},
+                            data_types::f32, format::bfyx};
+    auto input_mem = engine.allocate_memory({{static_cast<int64_t>(dim_b), static_cast<int64_t>(dim_c),
+                                              static_cast<int64_t>(dim_h), static_cast<int64_t>(dim_w)},
+                                             data_types::f32, format::bfyx});
+    auto axis_mem = engine.allocate_memory({{}, data_types::i64, format::bfyx});
+    auto splits_length_mem = engine.allocate_memory({{2}, data_types::i64, format::bfyx});
+
+    const int64_t axis = 0;
+
+    auto input_data = rg.generate_random_1d<float>(input_mem->count(), -1.f, 1.f);
+    set_values(input_mem, input_data);
+    set_values<int64_t>(axis_mem, {axis});
+    set_values<int64_t>(splits_length_mem, {1, 1});
+
+    // reshape [1, C, H, W] → [1, C, H*W]  (spatial flatten, batch preserved)
+    const int64_t hw = static_cast<int64_t>(dim_h * dim_w);
+    const std::vector<int64_t> flatten_pattern = {1, -1, hw};
+    const ov::PartialShape flatten_out_shape = {1, -1, hw};
+
+    cldnn::crop_ngraph_op_mode op_mode = cldnn::crop_ngraph_op_mode::variadic_split;
+    topology topology(
+        input_layout("input", in_layout),
+        data("axis", axis_mem),
+        data("splits_length", splits_length_mem),
+        // Branch 0: crop → [1, C, H, W] → reshape → [1, C, H*W]
+        crop("crop_0", {input_info("input"), input_info("axis"), input_info("splits_length")}, cldnn::tensor(1), cldnn::tensor(0), op_mode, 0, axis),
+        reshape("reshape_0", input_info("crop_0"), false, flatten_pattern, flatten_out_shape, cldnn::reshape::reshape_mode::base),
+        reorder("output_0", input_info("reshape_0"), format::bfyx, data_types::f32, std::vector<float>(), reorder_mean_mode::subtract, padding(), true),
+        // Branch 1: crop → [1, C, H, W] → reshape → [1, C, H*W]
+        crop("crop_1", {input_info("input"), input_info("axis"), input_info("splits_length")}, cldnn::tensor(1), cldnn::tensor(0), op_mode, 1, axis),
+        reshape("reshape_1", input_info("crop_1"), false, flatten_pattern, flatten_out_shape, cldnn::reshape::reshape_mode::base),
+        reorder("output_1", input_info("reshape_1"), format::bfyx, data_types::f32, std::vector<float>(), reorder_mean_mode::subtract, padding(), true)
+    );
+
+    auto config = get_test_default_config(engine);
+    config.set_property(ov::intel_gpu::allow_new_shape_infer(true));
+    config.set_property(ov::intel_gpu::optimize_data(true));
+    network network(engine, topology, config);
+    network.set_input_data("input", input_mem);
+
+    auto outputs = network.execute();
+
+    auto out0_mem = outputs.at("output_0").get_memory();
+    cldnn::mem_lock<float> out0(out0_mem, get_test_stream());
+    auto out1_mem = outputs.at("output_1").get_memory();
+    cldnn::mem_lock<float> out1(out1_mem, get_test_stream());
+
+    ASSERT_EQ(out0.size(), slice_elems);
+    ASSERT_EQ(out1.size(), slice_elems);
+
+    // Branch 0 must read the first batch slice
+    for (size_t i = 0; i < slice_elems; i++)
+        ASSERT_FLOAT_EQ(out0[i], input_data[0 * slice_elems + i]) << "Branch 0 mismatch at " << i;
+    // Branch 1 must read the second batch slice
+    for (size_t i = 0; i < slice_elems; i++)
+        ASSERT_FLOAT_EQ(out1[i], input_data[1 * slice_elems + i]) << "Branch 1 mismatch at " << i;
+}