One of the most serious mining disasters in underground mines is rockburst phenomena. They can lead to injuries and even fatalities as well as damage to underground openings and mining equipment. This has forced many researchers to investigate alternative methods to predict the potential for rockburst occurrence. However, due to the highly complex relation between geological, mechanical and geometric parameters of the mining environment, the traditional mechanics-based prediction methods do not always yield precise results. With the emergence of machine learning methods, a breakthrough in the prediction of rockburst occurrence has become possible in recent years. This paper presents a state-of-the-art review of various applications of machine learning methods for the prediction of rockburst potential. First, existing rockburst prediction methods are introduced, and the limitations of such methods are highlighted. A brief overview of typical machine learning methods and their main features as predictive tools is then presented. The current applications of machine learning models in rockburst prediction are surveyed, with related mechanisms, technical details and performance analysis.