Abstract: As the data sharing and availability in environmental and health research continue to improve, the number of large datasets for environmental and human health has increased dramatically. However, these large environmental health datasets are diverse and complex, and traditional epidemiological and environmental health models are difficult to effectively analyze, leading to the development of a new approach to environmental health research. The application of artificial intelligence (AI) technology in environmental health is rapidly developing, providing novel and powerful tools for new pollutant screening and toxicity prediction, biomonitoring, risk assessment, and health protection. Among them, advanced machine learning (ML) algorithms can reveal laws that are difficult for humans to detect, showing important potential in biomarker identification, disease prevention, and environmental engineering optimization. This can provide new ideas and breakthroughs for environmental health research and technological innovation. However, the application of ML technology in the field of environmental health still faces challenges such as data quality, model interpretability, and interdisciplinary cooperation. This paper will review the latest progress in the application of ML technology in the field of environmental health, discuss its advantages, challenges, and future development directions, with the aim of providing valuable references for research and practice in the fields of environmental protection and public health.