With the rapid development of hydrogen energy technology, metal equipment is increasingly used in hydrogen environments. However, the hydrogen embrittlement effect will significantly weaken the fatigue performance of metal materials, posing hidden dangers to the safety of related equipment. Therefore, it is of great significance to accurately predict the fatigue life of metal materials in a hydrogen environment. This paper systematically analyzed the fatigue crack growth behavior of metal materials in a hydrogen environment and summarized the effects of various parameters on the fatigue crack growth rate under the hydrogen embrittlement effect. At the same time, the research on fatigue properties of metal materials in a hydrogen environment and the application of fatigue life prediction methods were investigated. The fatigue crack growth rate of metal materials in hydrogen environment can be used as an input to calculate the fatigue life of materials, but research has found that the fatigue crack growth rate is affected by a variety of parameters. Although the method based on fracture mechanics is commonly used in the fatigue crack growth stage and serves as a commonly used theory for fatigue life prediction in a hydrogen environment, its solution efficiency needs to be improved. With its efficient and accurate prediction performance, machine learning is widely used in the life prediction of various fatigue problems. However, it is still less applied in the field of fatigue life prediction of metal materials in a hydrogen environment. If relevant data enhancement methods can be used to expand fatigue life data in hydrogen environments, machine learning-based methods can be used for life prediction, which may significantly improve the efficiency of fatigue life prediction of metal materials in hydrogen environments.