Groundwater resources play an important role in water resources of all countries in the world. They are essential for human survival, development and maintenance of health of ecosystem. At this stage, groundwater pollution is becoming increasingly serious. The research on groundwater environmental background value and pollution risk assessment is of great significance for the prevention and control of groundwater pollution. The factors influencing groundwater pollution are complex, including heterogeneity in spatial distribution of groundwater chemical components, small groundwater sample size and high calculation cost of large scale areas. These limitations pose a great challenge in the traditional pollution risk assessment methods. As the core of artificial intelligence, machine learning has recently become a frontier hot spot in hydrogeology research, newer approaches using intelligent and efficient data processing and mining have explored the distribution, variation and occurrence mechanism of groundwater chemical components. This paper comprehensively introduces the machine learning methods applied in groundwater pollution research in recent years, including unsupervised learning algorithm based on clustering, supervised learning algorithms based on regression, hybrid algorithm aimed at improving algorithm efficiency, and depth structure algorithm with neural network as the core, showing the achievements of different types of algorithms in groundwater pollution research. The mechanism of each algorithm is summarized in detail, the technical advantages and disadvantages of the algorithm and the applicable direction are discussed. Finally, the application and development trend of machine learning in the field of groundwater pollution are evaluated. It is suggested to explore an efficient integrated learning model to make up for the shortcomings of a single algorithm. At the same time, the deep learning modeling technology for small samples is developed to improve the accuracy of groundwater pollution assessment, and expand and enrich the application of new methods and technologies in groundwater pollution research.