With the emergence of high-throughput technology and massive toxicology data,toxicology research has entered the era of big data.How to efficiently integrate existingtoxicological data,clarify the toxic effects of chemicals,and use these patterns to providenew information,in order to achieve effi-cient prediction of the toxicity of new chemicalsubstances,is one of the cutting-edge issues in toxicology.In view of the high cost,low throughput and difficulty in revealing the mechanism information of tradi-tional chemical toxicity testing methods,high throughput prediction models are urgently needed.Machine learning methods have been applied to toxicity testing,such as supervised learning models,unsupervised learning models,deep learning models,reinforcement learning models,and transfer learning models.Chemical characteristic data commonly used in machine learning models include chemical structure data,text data,toxicological genome data and image data.There is huge potential for applying machine learning to toxicity testing and machine learning methods have made some prog-ress.However,current research focuses on the processing of data and development of models,which has failed to produce a widely used and accepted method.In addition,the prediction accuracy of machine learning models is not only dependent on algorithms,but also affected by data quality,and the mutual promotion and development of algorithms and data quality remains a big challenge.In short,data processing and model construction in the field of toxicology require interdisciplinary cooperation and technological innovation.With the increasing perfection of toxicology databases and the continuous optimization of various model algorithms,the toxicity prediction of new chemicals based on machine learning models will become increasingly efficient and accurate,playing an important role in ensuring human health and environmental safety.