China's commitment to peak carbon emissions by 2030 and achieve carbon neutrality by 2060 has spurred advancements in clean energy technologies. One significant area of focus is hydrogen production from coal using Coal Supercritical Water Gasification (SCWG) technology. This process efficiently converts organic matter in coal into hydrogen-rich gas, offering a clean energy source while reducing greenhouse gas emissions. Researchers have made strides in understanding the gasification characteristics, catalyst effects, and kinetics of coal SCWG. These studies have highlighted the importance of temperature and catalysts in enhancing gasification efficiency. While coal SCWG shows promise, the experimental process is resource-intensive. To address this, researchers are turning to Machine Learning (ML) algorithms to optimize hydrogen production processes. ML models such as Decision Trees and Artificial Neural Networks have been successful in predicting gas yields and compositions in SCWG experiments, offering accurate and efficient analyses. The application of ML in coal SCWG not only streamlines experimentation but also contributes to a more sustainable approach to clean energy production. Overall, the integration of advanced technologies like ML into the field of coal SCWG showcases a positive outlook for the future of hydrogen production in China. These innovations not only enhance efficiency and reduce environmental impact but also pave the way for a cleaner and more sustainable energy system.