The water environmental risks of emerging contaminants (ECs) pose a severe challenge to conventional water treatment technologies. Advanced oxidation processes (AOPs) rely on the potent oxidative efficacy of reactive oxygen species (ROS). However, their vast variety, complex reaction pathways, and transient existence cause significant bottlenecks for traditional experimental methods in systematically elucidating the generation laws and action mechanisms of ROS, as well as the structure-activity relationships of high-efficiency catalysts. As a powerful data mining and pattern recognition tool, machine learning (ML) is fundamentally changing the research paradigm of AOPs, providing unprecedented solutions for the precise analysis and targeted design of this complex system. This paper systematically summarized the three core applications of ML in AOP research, namely, the prediction and optimization of reaction processes and ROS kinetics, the deep analysis of complex reaction pathways and microscopic mechanisms, and the intelligent screening and rational design of high-performance catalysts. The content covers radical pathways such as hydroxyl radicals (HO·) and sulfate radicals (SO-₄·), as well as non-radical systems such as singlet oxygen (¹O₂) and high-valent metal species. The results indicate that by integrating multi-source data and physicochemical models, ML not only achieves the accurate prediction and optimization of the performance of AOPs but also demonstrates strong capabilities in revealing implicit structure-activity relationships, discovering new descriptors, and predicting novel catalysts, promoting the transition of this field from "trial-and-error method" to "rational design". The focus of breakthroughs in this field will be on developing ML models with stronger interpretability and integrated physical mechanisms and establishing standardized databases to ultimately realize the intelligent and customized design and application of AOPs.