Rockburst is a common ground pressure hazard in underground geotechnical engineering. To predict rockburst accurately in real-time, this study proposes a short-term rockburst prediction model based on microseismic monitoring and probability optimization naive Bayes. Firstly, based on 114 sets of rockburst sample data, four microseismic parameters were selected as predictors using the correlation feature selection algorithm: cumulative number of microseismic events, cumulative microseismic energy, cumulative microseismic apparent volume, and cumulative microseismic energy rate. Secondly, to weaken the conditional independence assumption of the naive Bayes algorithm, the criteria importance through intercriteria correlation method and the similarity function are used to optimize the conditional probability in terms of both attribute weighting and instance weighting. The Mahalanobis distance is introduced to compensate for the loss of prior probability, addressing the decision imbalance caused by conditional probability weighting. Thus, a probability optimization naive Bayes algorithm with conditional probability weighting and prior probability compensation mechanism is proposed to predict the rockburst intensity levels. Finally, the model’s accuracy and reliability are tested through model evaluation, model comparison, and engineering validation. The results show that the proposed model has a prediction accuracy of 86.96% and outperforms other machine learning models, providing a scientific basis for rockburst prediction in practical engineering.