Ensuring a high-quality, safe, and reliable drinking water supply is a necessary guarantee for sustainable socio-economic development. Disinfection is an indispensable process in drinking water treatment. The disinfection process must effectively inactivate pathogens, strictly control disinfectant residuals and byproduct generation, and achieve collaborative control of biological and chemical risks. The effect of drinking water disinfection is influenced by multiple factors including environmental conditions, water quality background, disinfection methods, and standard requirements, making its efficacy control particularly difficult. Consequently, the disinfection process exhibits higher complexity, placing higher demands on the refined and dynamic regulation of operation. In recent years, the rapid development of artificial intelligence (AI) technology has provided new ideas for the optimization of drinking water disinfection processes and the control of derived risks. This paper systematically elaborated on the application of AI-driven multi-source data fusion in improving the efficacy of disinfection processes. By taking key links such as precursor risk identification, disinfection efficacy assessment, byproduct prediction, and water quality safety screening as objects, this paper systematically reviewed the technical methods for applying dynamic learning algorithms in improving microbial inactivation effects, controlling disinfection byproduct generation, and rapidly screening toxicological risks and proposed strategies and development directions for the application of AI technology in disinfection processes. With the continuous development of AI technology and the wide application of big data, AI will play a more important role in the optimal application of disinfection processes, ensuring the biological and chemical safety of water quality and coping with complex water quality problems.