diff --git a/dpnp/backend/extensions/blas/gemm_batch.cpp b/dpnp/backend/extensions/blas/gemm_batch.cpp
index 6a1247c4c3e6..0d8ad1a67432 100644
--- a/dpnp/backend/extensions/blas/gemm_batch.cpp
+++ b/dpnp/backend/extensions/blas/gemm_batch.cpp
@@ -166,6 +166,45 @@ static sycl::event gemm_batch_impl(sycl::queue &exec_q,
     return gemm_batch_event;
 }
 
+void standardize_strides_to_nonzero(std::vector<py::ssize_t> &strides,
+                                    const py::ssize_t *shape)
+{
+    // When shape of an array along any particular dimension is 1, the stride
+    // along that dimension is undefined. This function standardize the strides
+    // by calculating the non-zero value of the strides.
+    std::size_t ndim = strides.size();
+    bool has_zero_stride = std::accumulate(strides.begin(), strides.end(), 1,
+                                           std::multiplies<py::ssize_t>{}) == 0;
+
+    if (has_zero_stride) {
+        for (std::size_t i = 0; i < ndim - 1; ++i) {
+            strides[i] = strides[i] == 0
+                             ? std::accumulate(shape + i + 1, shape + ndim, 1,
+                                               std::multiplies<py::ssize_t>{})
+                             : strides[i];
+        }
+        strides[ndim - 1] = strides[ndim - 1] == 0 ? 1 : strides[ndim - 1];
+    }
+}
+
+void standardize_strides_to_zero(std::vector<py::ssize_t> &strides,
+                                 const py::ssize_t *shape)
+{
+    // When shape of an array along any particular dimension is 1, the stride
+    // along that dimension is undefined. This function standardize the strides
+    // by defining such a stride as zero. This is because for these cases,
+    // instead of copying the array into the additional dimension for batch
+    // multiplication, we choose to use zero as the stride between different
+    // matrices.  Therefore, the same array is used repeatedly.
+    std::size_t ndim = strides.size();
+
+    for (size_t i = 0; i < ndim; ++i) {
+        if (shape[i] <= 1) {
+            strides[i] = 0;
+        }
+    }
+}
+
 std::tuple<sycl::event, sycl::event, bool>
     gemm_batch(sycl::queue &exec_q,
                dpctl::tensor::usm_ndarray matrixA,
@@ -240,10 +279,15 @@ std::tuple<sycl::event, sycl::event, bool>
     std::vector<py::ssize_t> a_stride = matrixA.get_strides_vector();
     std::vector<py::ssize_t> b_stride = matrixB.get_strides_vector();
     std::vector<py::ssize_t> c_stride = resultC.get_strides_vector();
+    standardize_strides_to_zero(a_stride, a_shape);
+    standardize_strides_to_zero(b_stride, b_shape);
+    standardize_strides_to_zero(c_stride, c_shape);
     const std::int64_t stridea = a_stride[0];
     const std::int64_t strideb = b_stride[0];
     const std::int64_t stridec = c_stride[0];
 
+    standardize_strides_to_nonzero(a_stride, a_shape);
+    standardize_strides_to_nonzero(b_stride, b_shape);
     bool A_base_is_f_contig = a_stride[1] == 1 && a_stride[2] == a_shape[1];
     bool B_base_is_f_contig = b_stride[1] == 1 && b_stride[2] == b_shape[1];
 
diff --git a/dpnp/dpnp_utils/dpnp_utils_linearalgebra.py b/dpnp/dpnp_utils/dpnp_utils_linearalgebra.py
index 8d4509fd0deb..5269114d609e 100644
--- a/dpnp/dpnp_utils/dpnp_utils_linearalgebra.py
+++ b/dpnp/dpnp_utils/dpnp_utils_linearalgebra.py
@@ -297,6 +297,7 @@ def _define_contig_flag(x):
     if x.ndim < 2:
         return True
 
+    x_strides = _standardize_strides_to_nonzero(x_strides, x_shape)
     x_is_c_contiguous = x_strides[-1] == 1 and x_strides[-2] == x_shape[-1]
     x_is_f_contiguous = x_strides[-2] == 1 and x_strides[-1] == x_shape[-2]
     if x_is_c_contiguous or x_is_f_contiguous:
@@ -1371,6 +1372,28 @@ def _shape_error(a, b, core_dim, err_msg):
         )
 
 
+def _standardize_strides_to_nonzero(strides, shape):
+    """
+    Standardizing the strides.
+    When shape of an array along any particular dimension is 1, the stride
+    along that dimension is undefined. This function standardize the strides
+    by calculating the non-zero value of the strides.
+    """
+
+    ndim = len(strides)
+    if numpy.prod(strides) == 0:
+        stndrd_strides = tuple(
+            numpy.prod(shape[i + 1 :]) if strides[i] == 0 else strides[i]
+            for i in range(ndim - 1)
+        )
+        last_stride = 1 if strides[ndim - 1] == 0 else strides[ndim - 1]
+        stndrd_strides += (last_stride,)
+    else:
+        stndrd_strides = strides
+
+    return stndrd_strides
+
+
 def _transpose_ex(a, axeses):
     """
     Copied from _transpose_ex in cupy/core/_einsum.py