Rockburst in deep tunnels is a hazard during the underground engineering construction. Accurate early warning of rockburst can protect the lives and properties of engineering personnel. The intelligent technologies such as machine learning （ML） have brought new ideas and methods for rockburst early warning, which has improved the accuracy, timeliness and intelligence for early warning of rockburst. A systematic study on the current application of ML in microseismic （MS） monitoring and early warning of rockbursts in deep tunnels was carried out. Firstly, ML algorithms in the MS monitoring, evaluation and early warning of rockbursts were summarized. The characteristic advantages of the various types of ML algorithms were analyzed. Then, the indicator system for MS monitoring and early warning of rockburst was discussed. The applications of MS monitoring and early warning of rockburst based on different ML methods and their effects were analyzed. The results show that neural network （NN） is one of the most popular algorithms for rockburst warning, the MS event（N）, MS energy（E）, MS apparent volume （V） and its variants are the most frequently used MS parameters, and most of the rockburst warning parameters are between 3~7 in number. Rockburst intensity is the research hotspot of rockburst warning, and the warning accuracy based on most ML methods can reach 80%, which indicates that the ML method has good application effects and development prospects. Finally, prospects were made for the development direction of ML in MS monitoring and early warning for the rockburst in deep tunnels, i.e. advanced ML algorithms, the accuracy and comprehensiveness of the early warning indicator system, the richness of the sample, the time warning of the rockburst occurrence, and the capability of data processing to be further investigated in depth.