Abstract: Solar energy-driven photocatalytic H₂ production, as a key technology in the clean conversion and utilization of energy field, has the potential to achieve efficient utilization of solar energy and low-cost green H₂ production, and has been paid extensive attention. Until now, although the research methods based on traditional empirical trial-and-error have realized enhanced photocatalytic efficiencies for H₂ production that are far from meeting the required efficiency of industrial application, severely restricting its future large-scale application. Machine learning, by establishing a data-driven intelligent analysis paradigm, is driving the field of photocatalytic H₂ production to shift from the traditional trial-and-error based on experience to a research model of precise prediction, and expected to promote the breakthroughs in photocatalytic H₂ production. Accordingly, this paper systematically reviews the applications and research progress of machine learning in photocatalytic H₂ production: Firstly, the core elements of the machine learning technology framework are explained, with a particular focus on the construction strategy of the photocatalysis dataset and the optimization path of feature engineering （covering physical-chemical descriptor screening, high-dimensional feature dimension reduction, and knowledge embedding methods, etc.）. Secondly, the application of machine learning in the field of photocatalysis is discussed. In terms of optimizing material performance, a systematic summary is provided of the breakthroughs made by machine learning in key aspects such as precise regulation of band structure, improvement of carrier migration efficiency, and intelligent design of interface heterojunctions. At the level of experimental process innovation, the focus is on exploring dynamic parameter optimization algorithms based on machine learning, intelligent high-throughput experimental platforms, and adaptive model construction with real-time feedback capabilities. Finally, it is proposed that the future development of machine learning in this field should focus on building a new research paradigm driven by both data and mechanism, developing interpretable models with physical constraints, and constructing cross-scale dynamic simulation systems. Ultimately, it will provide full-chain intelligent optimization for the research and development of photocatalytic hydrogen production technology, from microscopic mechanisms to macroscopic processes.