diff --git a/modules/dnn/src/layers/nary_eltwise_layers.cpp b/modules/dnn/src/layers/nary_eltwise_layers.cpp
index d9374a6b93..ec0fa6b0b4 100644
--- a/modules/dnn/src/layers/nary_eltwise_layers.cpp
+++ b/modules/dnn/src/layers/nary_eltwise_layers.cpp
@@ -51,6 +51,8 @@ public:
         WHERE,
     } op;
 
+    // If the eltwise implementation is modified, you need to force enable the 'Layer_Test_Eltwise_bcast'
+    // test in the 'test_layers.cpp' file to make sure it all passes
     NaryEltwiseLayerImpl(const LayerParams& params)
     {
         setParamsFrom(params);
@@ -350,7 +352,6 @@ public:
         size_t dp1 = steps[1][ndims-1]/sizeof(T);
         size_t dp2 = steps[2][ndims-1]/sizeof(T);
 
-        CV_Assert(dp == 1);
         enum { BLOCK_SIZE = 1024 };
         T blck[BLOCK_SIZE];
 
diff --git a/modules/dnn/test/test_layers.cpp b/modules/dnn/test/test_layers.cpp
index 6afb3a8aa0..583d6dc050 100644
--- a/modules/dnn/test/test_layers.cpp
+++ b/modules/dnn/test/test_layers.cpp
@@ -1937,6 +1937,117 @@ INSTANTIATE_TEST_CASE_P(/**/, Layer_Test_Eltwise_unequal, Combine(
     dnnBackendsAndTargets()
 ));
 
+
+struct Layer_Test_Eltwise_bcast : testing::TestWithParam<tuple<string, int, tuple<Backend, Target>>>
+{
+public:
+    void test_bcast()
+    {
+        string op = get<0>(GetParam());
+        int dim = get<1>(GetParam());
+        tuple<Backend, Target> backend_target= get<2>(GetParam());
+        int backend = get<0>(backend_target);
+        int target = get<1>(backend_target);
+
+        vector<vector<int>> dim_shape_list;
+        get_all_arr(dim_shape_list, dim);
+        replace(dim_shape_list, 1, 3);
+        // same shape
+        for (int i = 0; i < dim_shape_list.size(); i++)
+            for (int j = 0; j < dim_shape_list.size(); j++)
+                run(dim_shape_list[i], dim_shape_list[j], op, backend, target);
+
+        vector<vector<int>> sub_shape_list;
+        vector<vector<int>> tmp;
+        for(int i = 1; i < dim; i++){
+            get_all_arr(tmp, i);
+            replace(tmp, 1, 3);
+            sub_shape_list.insert(sub_shape_list.end(), tmp.begin(), tmp.end());
+        }
+
+        // diff shape
+        for (const auto &shp1: dim_shape_list)
+            for (const auto &shp2: sub_shape_list)
+                run(shp1, shp2, op, backend, target);
+
+        // diff shape
+        for (const auto &shp1: sub_shape_list)
+            for (const auto &shp2: dim_shape_list)
+                run(shp1, shp2, op, backend, target);
+    }
+
+private:
+    // give n to generate all n-D arrays with 0 or 1
+    static void get_all_arr(vector<vector<int>> &arr, int n)
+    {
+        int total = 1 << n;
+        arr.assign(total, vector<int>(n, -1));
+        for (int i = 0; i < total; i++)
+            for (int j = 0; j < n; j++)
+                arr[i][j] = (i >> (n - j - 1)) & 1;
+    }
+
+    // zero will replace all 0, one will replace all 1
+    static void replace(vector<vector<int>> &arr, int zero, int one)
+    {
+        for (int i = 0; i < arr.size(); i++)
+            for (int j = 0; j < arr[0].size(); j++)
+                arr[i][j] = arr[i][j] ? one : zero;
+    }
+
+    static void run(const vector<int> &a_shape, const vector<int> &b_shape, const String &op, const int backend, const int target)
+    {
+        Mat a = Mat::zeros((int) a_shape.size(), a_shape.data(), CV_32FC1);
+        Mat b = Mat::ones((int) b_shape.size(), b_shape.data(), CV_32FC1);
+
+        Net net;
+        LayerParams lp;
+        lp.type = "NaryEltwise";
+        lp.name = "testLayer";
+        lp.set("operation", op);
+        int id = net.addLayerToPrev(lp.name, lp.type, lp);
+        net.connect(0, 1, id, 1);
+
+        vector<String> inpNames(2);
+        inpNames[0] = "a";
+        inpNames[1] = "b";
+        net.setInputsNames(inpNames);
+        net.setInput(a, inpNames[0]);
+        net.setInput(b, inpNames[1]);
+
+        net.setPreferableBackend(backend);
+        net.setPreferableTarget(target);
+
+        Mat re;
+        ASSERT_NO_THROW(re = net.forward()); // runtime error
+        auto ptr_re = (float *) re.data;
+        for (int i = 0; i < re.total(); i++)
+            if (op == "sum"){
+                ASSERT_EQ(1, ptr_re[i]); // sum result should be 1
+            }
+    }
+};
+
+TEST_P(Layer_Test_Eltwise_bcast, DISABLED_brute_force)
+{
+    test_bcast();
+}
+
+// This test is to verify whether the broadcast operations of unidirectional and bidirectional,
+// as well as tensors with same and different shapes, can be forwarded correctly.
+// This can ensure that the elementwise layer does not have any errors when forwarding.
+//
+// To test which cases the backend will fallback to the cpu, replace the fallback command like
+// `return Ptr<BackendNode>();` in `initCUDA()` with `throw std::runtime_error("fallback");`
+//
+// To test more operators, add more ops after "sum".
+// Default only "sum" is tested, because for the most cases they have the same implementation.
+INSTANTIATE_TEST_CASE_P(/**/, Layer_Test_Eltwise_bcast, Combine(
+        Values("sum"),
+        Values(1, 2, 3, 4, 5),
+        dnnBackendsAndTargets()
+));
+
 typedef testing::TestWithParam<tuple<Backend, Target> > Layer_Test_Resize;
 TEST_P(Layer_Test_Resize, change_input)
 {