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Gas outburst poses a huge threat to the safe production of coal mines. Therefore, the prediction of gas outburst has always been a hot topic for researchers. In recent years, the use of artificial intelligence algorithms for gas outburst prediction has made progress, such as using BP neural network, GA algorithm, and SVM algorithm. Despite these progresses, predicting the gas outburst more accurately and efficiently still remains a great challenge. In this work, an algorithm based on grey relational analysis and SVM using adaptive particle swarm optimization (APSO-SVM) for gas outburst prediction is proposed. Grey relational analysis was used to extract the four most relevant ones from nine gas outburst prediction parameters (geological structure zone distance, coal seam gas content, gas release initial velocity, gas desorption index-K1, drill cuttings volume, coal seam depth, coal seam thickness, coal destruction type, and coal firmness coefficient) to give the needed parameters for SVM model. Higher prediction accuracy was then obtained with the selected parameters composed by coal seam gas content, gas release initial velocity, gas desorption index-K1, and drill cuttings volume. Moreover, adaptive particle swarm optimization (APSO) was used to optimize the penalty factor and kernel parameters of the support vector machine to improve the global search ability and avoid the occurrence of the local optimal solutions. The APSO-SVM model was applied to the prediction of gas outburst in 31004 tunneling face of Xinyuan Coal Mine, Yangquan City, Shanxi Province, China. We further introduced the criteria of accuracy, precision, recall, and F 2 -score to evaluate the prediction results of different models. The results show that, in the gas outburst prediction, the accuracy of the APSO-SVM model is 98.38%, the precision and recall are both 100%, and F 2 -score is 1. Comparative studies confirm that APSO-SVM displayed better performance than SVM and PSO-SVM models for the applied grey relational analysis assisted gas outburst prediction. These obtained results indicate the validity of APSO-SVM model for gas outburst prediction. [ABSTRACT FROM AUTHOR]