Abstract: The cathodic protection system is essential for preventing and controlling corrosion in pipelines. However, traditional monitoring methods face challenges, including low data utilization rates and delays in identifying hidden issues. To address these concerns, this paper examines the characteristics of cathodic protection data and data mining technology. It elaborates on the logical relationships among cathodic protection data, while exploring the mechanisms, functions, and modes of mainstream algorithms and large artificial intelligence (AI) models used for big data mining in the corrosion field. Additionally, the paper summarizes current applications of data mining in pipeline cathodic protection both in China and abroad. This study reveals the following findings: ①The integration of cathodic protection with big data and AI has great potential to elevate traditional experience-based corrosion protection to a new level, characterized by scientific prediction and proactive control. ②Future cathodic protection systems will enhance the reliability of infrastructure protection through more intelligent and adaptive responses to environmental changes, leading to more effective system optimizations. ③From a research perspective, it is essential to further improve algorithm models for cathodic protection scenarios, focusing on enhancing their accuracy and interpretability. From an application standpoint, it is necessary to verify the effectiveness of these technologies in more real-world projects while accumulating experience and lessons. The conclusion is that data mining technology can be leveraged to facilitate the transformation of the oil and gas industry toward a predictive maintenance model by significantly enhancing the management level of cathodic protection systems, thereby demonstrating its important research value.