The purpose of this work is to establish a hammering echo inspection technology capable of detecting damage accurately irrespective of the skill of the inspector. To realize this technology, we have proposed and developed an “artificial intelligence (AI)-aided hammering test system” that automatically identifies the anomalous parts of a structure and the extent of the anomalies via the machine learning of the differences in hammering echoes. A laser range sensor is used to easily identify the hitting position of the hammer and integrate this information into the hammering echo analysis results to automatically generate an anomaly map. We performed hammering echo collection experiments using the AI-aided hammering test system and evaluated its performance. In the experiments, we inspected seven actual bridges in which internal defects (float) were detected by a detailed manual hammering test and compared the results with those obtained using our system. No defects were missed in a coarse block unit, and the accuracy for each hammering echo was determined to be 96.3% at maximum and 90.4% on average.