Machine learning technology is increasingly recognized as a significant trend in the investigation of toxicity prediction for emerging contaminants, owing to its robust capabilities in data processing and pattern recognition. This paper provides a comprehensive review of the advancements in machine learning applications for predicting the toxicity of Emerging Contaminants. It examines the current state of technological development concerning data collection and preparation, model training, and evaluation. Furthermore, the paper emphasizes the critical role of interpreting prediction results in the context of practical applications. The implementation of machine learning models for toxicity prediction not only enhances efficiency considerably but also increases prediction accuracy through ongoing algorithm optimization.