Concrete cracking is the most typical defect that can be found in a reinforced concrete structure. The current approach to the concrete cracking identification was generally focused on visual inspection. This approach is time-consuming, very subjective and conventional in the current technology era. Therefore, the integration of advanced approaches such as artificial intelligence (AI) offers better and promising results as compared to the conventional approach. This study focused on the algorithm development of concrete crack detection using AI tool. A random angle and method of photogrammetry have been utilized in data collection by using a Smartphone camera. The AI tool used in this study is a YOLOv8 analysis software. On the other hand, additional data has also been collected from images taken from an open-sourced website. The results show that an average precision of 81.77% has been reported based on the collected concrete crack data. This value shows high precision and highlights the potential of YOLO analysis software as a cracking detection tool. This indicates strong potential of YOLO analysis model as a practical engineering application to enhance the conventional inspection into something more consistent, speedy and reliable of concrete cracking identification.