This study introduces a transformative artificial intelligence of things (AIoT) framework that advances bridge maintenance by incorporating advanced inspection techniques. A central innovation is the Pilgrimage Walk Optimization (PWO)-Lite algorithm, which fine-tunes the hyperparameters of the You Only Look Once (YOLO)v7-tiny deep learning model. This model, integrated with the Deep Simple Online and Realtime Tracking (DeepSORT) algorithm, enables real-time detection and significantly enhances the system’s ability to detect deteriorations in concrete beneath bridge decks swiftly and accurately. The PWO-Lite algorithm draws inspiration from the traditional Matsu pilgrimage, an important Taiwanese folk religious event. It reflects this influence in its search behavior, miming devotees’ gathering and movement patterns. This unique approach to algorithmic design incorporates cultural customs into computational strategies. An embedded system has been configured to efficiently process visual data from unmanned aerial vehicles (UAVs), providing actionable insights directly at the inspection site. This configuration reduces the reliance on heavy computational equipment and complex setups, streamlining bridge inspections and minimizing dependence on extensive infrastructure. The practical integration of this technology into UAVs allows engineers and field professionals to obtain precise, real-time data, enhancing maintenance planning and resource management. The broader implications of this research include the potential to significantly improve standard practices in infrastructure maintenance, offering a scalable solution that could revolutionize the field. This study bridges the gap between traditional AI applications and civil engineering. It introduces a culturally inspired optimization technique to structural health monitoring, benefiting both theoretical and practical aspects of infrastructure maintenance.