Abstract: This paper reviews the main technologies in coal and gas outburst prediction, including the application of machine learning and optimization algorithms, focusing on the current technical challenges, their solutions, and future research directions.Current research shows that machine learning techniques, such as Support Vector Machines(SVM),Random Forests(RF),Artificial Neural Networks(ANN),and deep learning models, have been widely applied to real-time evaluation of gas concentration prediction.Machine learning models can effectively handle large-scale datasets, analyze nonlinear relationships within the data, and improve prediction accuracy and efficiency.However, data scarcity, sample imbalance, and small sample issues remain major challenges in coal and gas outburst prediction.To address these issues, the paper proposes data augmentation, data cleaning, and cross-mine data sharing to improve data quality; by resampling techniques, cost-sensitive learning, and ensemble methods, sample imbalance is addressed; and by transfer learning, Bayesian optimization, and data simulation techniques, small sample limitation is overcome.Future research should delve into algorithm and model innovation, interdisciplinary studies, and the development of real-time monitoring and prediction systems to further enhance prediction accuracy and efficiency, ensuring the safe mining of coal mines.