Abstract: The safety in the coal-producing mines in China is continuously improving, but coal and gas outburst accidents still occur. The prediction of coal and gas outbursts allows the scientific application of outburst prevention measures, which can ensure the safe coal mining to a certain extent. Machine learning is an interdisciplinary field involving probability theory, statistics, and computer science, which can explore the nonlinear relationship between outburst accidents and its associated indicators. The application of machine learning in coal and gas outburst prediction has received relatively widespread attention, and with the rapid progress of artificial intelligence and computer technology, it will play a greater role in the field of outburst prediction. Therefore, this paper provides a comprehensive review of the research on machine learning in coal and gas outburst prediction, analyzes the difficulties in outburst prediction and prospects its development direction. Firstly, the paper provides a brief overview of the research status on the hypothesis, occurrence mechanism, and prediction index selection of coal and gas outbursts. Then, it summarizes the research progress in the field of outburst prediction, including the application of support vector machines, neural networks, extreme learning machines, and ensemble learning algorithms. In addition, it also points out the existing problems in the current research, such as imbalanced samples, missing data indicators, and small sample sizes. Finally, the paper gives an outlook on the developments of coal and gas outburst prediction based on machine learning, including improving algorithm performance, optimizing feature engineering, and increasing sample size. With the continuous improvement of computer performance, more powerful models may be proposed, which can further improve the prediction accuracy of outburst accidents.