我国煤矿安全生产形势不断好转，但煤与瓦斯突出事故仍时有发生。煤与瓦斯突出预测不仅能指导防突措施科学的运用、减少防突措施工程量，在一定程度上也可以确保煤矿工人的作业安全。机器学习(Machine Learning, ML)是一门涉及概率论、统计学和计算机学等领域的交叉学科，可以挖掘突出事故和指标间的非线性关系。将机器学习用于煤与瓦斯突出预测，已得到相对广泛的关注，并随着人工智能和计算机技术的快速进步，其在突出预测领域将发挥更大作用。因此，对机器学习在煤与瓦斯突出预测中的研究进行了全面的综述，分析其在突出预测中面临的难点并展望其发展方向。首先，简述煤与瓦斯突出假说、发生机理与预测指标选择的研究现状；介绍机器学习在煤与瓦斯突出预测领域的主要研究进展，包括支持向量机(Support Vector Machines, SVM)、人工神经网络(Artificial Neural Network, ANN)、极限学习机(Extreme Learning Machines, ELM)和集成学习(Ensemble Learning, EL)等算法的应用，以及特征选择和缺失数据填补在数据处理等方面的创新，同时也指出了目前基于机器学习的突出预测研究面临的挑战及存在的问题，例如事故与非事故样本的不平衡、数据的指标缺失和机器学习中的小样本等；最后，展望了基于机器学习的煤与瓦斯突出预测的未来发展方向，包括改进算法性能、优化特征工程和增加样本量等。随着计算机性能的提升，有望开发出更为复杂、精准的模型，以提高对突出事故的准确预测能力。

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.