diff --git a/c/src/ml-api-inference-single.c b/c/src/ml-api-inference-single.c
index 03df6326..5c52350d 100644
--- a/c/src/ml-api-inference-single.c
+++ b/c/src/ml-api-inference-single.c
@@ -1015,6 +1015,33 @@ _ml_nnfw_to_str_prop (const ml_nnfw_hw_e hw)
   return str_prop;
 }
 
+/**
+ * @brief Specifies whether input/output tensor information is required for each neural network framework
+ * @param nnfw_type The neural network framework type (e.g., TensorFlow, PyTorch)
+ * @param requires_in_info TRUE if the framework requires input tensors information
+ * @param requires_out_info TRUE if the framework requires output tensors information
+ */
+typedef struct
+{
+  ml_nnfw_type_e nnfw_type;
+  gboolean requires_in_info;
+  gboolean requires_out_info;
+} nnfw_tensors_info;
+
+/**
+ * @brief A table defining frameworks that require tensor info setting.
+ * @details This table-driven approach simplifies adding or modifying framework-specific logic.
+ */
+static const nnfw_tensors_info nnfw_tensors_info_table[] = {
+  {ML_NNFW_TYPE_TENSORFLOW, TRUE, TRUE},
+  {ML_NNFW_TYPE_SNAP, TRUE, TRUE},
+  {ML_NNFW_TYPE_PYTORCH, TRUE, TRUE},
+  {ML_NNFW_TYPE_TRIX_ENGINE, TRUE, TRUE},
+  {ML_NNFW_TYPE_NCNN, TRUE, TRUE},
+  {ML_NNFW_TYPE_ARMNN, FALSE, FALSE}
+};
+
+
 /**
  * @brief Opens an ML model with the custom options and returns the instance as a handle.
  */
@@ -1031,6 +1058,9 @@ ml_single_open_custom (ml_single_h * single, ml_single_preset * info)
   g_autofree gchar *converted_models = NULL;
   gchar **list_models;
   guint i, num_models;
+  gboolean requires_in_info = FALSE;
+  gboolean requires_out_info = FALSE;
+  gboolean found_in_table = FALSE;
   char *hw_name;
 
   check_feature_state (ML_FEATURE_INFERENCE);
@@ -1097,59 +1127,50 @@ ml_single_open_custom (ml_single_h * single, ml_single_preset * info)
    * 3. Construct a direct connection with the nnfw.
    * Note that we do not construct a pipeline since 2019.12.
    */
-  if (nnfw == ML_NNFW_TYPE_TENSORFLOW || nnfw == ML_NNFW_TYPE_SNAP ||
-      nnfw == ML_NNFW_TYPE_PYTORCH || nnfw == ML_NNFW_TYPE_TRIX_ENGINE ||
-      nnfw == ML_NNFW_TYPE_NCNN) {
-    /* set input and output tensors information */
-    if (in_tensors_info && out_tensors_info) {
-      status =
-          ml_single_set_inout_tensors_info (filter_obj, TRUE, in_tensors_info);
-      if (status != ML_ERROR_NONE) {
-        _ml_error_report_continue
-            ("Input tensors info is given; however, failed to set input tensors info. Error code: %d",
-            status);
-        goto error;
-      }
+  /* Look up framework properties from the table */
+  for (i = 0; i < G_N_ELEMENTS (nnfw_tensors_info_table); i++) {
+    if (nnfw_tensors_info_table[i].nnfw_type == nnfw) {
+      requires_in_info = nnfw_tensors_info_table[i].requires_in_info;
+      requires_out_info = nnfw_tensors_info_table[i].requires_out_info;
+      found_in_table = TRUE;
+      break;
+    }
+  }
 
-      status =
-          ml_single_set_inout_tensors_info (filter_obj, FALSE,
-          out_tensors_info);
-      if (status != ML_ERROR_NONE) {
-        _ml_error_report_continue
-            ("Output tensors info is given; however, failed to set output tensors info. Error code: %d",
-            status);
-        goto error;
-      }
-    } else {
-      _ml_error_report
-          ("To run the given nnfw, '%s', with a neural network model, both input and output information should be provided.",
-          fw_name);
+  if (found_in_table) {
+    if (requires_in_info && !in_tensors_info) {
+      _ml_error_report ("Framework '%s' requires input information.", fw_name);
+      status = ML_ERROR_INVALID_PARAMETER;
+      goto error;
+    }
+
+    if (requires_out_info && !out_tensors_info) {
+      _ml_error_report ("Framework '%s' requires output information.", fw_name);
       status = ML_ERROR_INVALID_PARAMETER;
       goto error;
     }
-  } else if (nnfw == ML_NNFW_TYPE_ARMNN) {
-    /* set input and output tensors information, if available */
+
     if (in_tensors_info) {
-      status =
-          ml_single_set_inout_tensors_info (filter_obj, TRUE, in_tensors_info);
+      status = ml_single_set_inout_tensors_info (filter_obj, TRUE, in_tensors_info);
       if (status != ML_ERROR_NONE) {
         _ml_error_report_continue
-            ("With nnfw '%s', input tensors info is optional. However, the user has provided an invalid input tensors info. Error code: %d",
-            fw_name, status);
+              ("Input tensors info is given; however, failed to set input tensors info. Error code: %d",
+              status);
         goto error;
       }
     }
+
     if (out_tensors_info) {
-      status =
-          ml_single_set_inout_tensors_info (filter_obj, FALSE,
-          out_tensors_info);
+      status = ml_single_set_inout_tensors_info (filter_obj, FALSE, out_tensors_info);
       if (status != ML_ERROR_NONE) {
-        _ml_error_report_continue
-            ("With nnfw '%s', output tensors info is optional. However, the user has provided an invalid output tensors info. Error code: %d",
-            fw_name, status);
+       _ml_error_report_continue
+              ("Output tensors info is given; however, failed to set output tensors info. Error code: %d",
+              status);
         goto error;
       }
     }
+  } else {
+    _ml_logi("Framework '%s' not found in tensors info table, using default behavior", fw_name);
   }
 
   /* set accelerator, framework, model files and custom option */