The prediction of the risk of rockbursts in burst-prone grounds is turned into a challenging and vital mission for most underground projects that attract great interest from engineers and researchers. In this study, a hybrid technique, the artificial neural network (ANN) and artificial bee colony (ABC), neuro-bee model, was considered to create the sophisticated relationship between the risk of rockbursts in burst-prone grounds and its influencing factors. The establishment and validation of ANN models were implemented via a data set extracted from previous works, and the database covers 246 reliable rockburst cases. Six influencing factors were selected as input variables. Five-fold cross validation were adopted to tune hyper-parameters of ABC-ANN models, and the performance of ANN models was evaluated by correlation coefficient ( R 2 ) and root mean square error (RMSE). Observational experiment results indicated that the ABC-ANN algorithm can be utilized as an effective tool for predicting the risk of rockbursts in burst-prone grounds. The R 2 and RMSE values between the predicted and actual rockburst values were 0.9656 and 0.1281, respectively. Sensitivity analyses implemented by the response surface method revealed that the maximum tangential stress of the cavern wall and the elastic strain index parameters have a greater effects on rockburst compared with other input parameters. As a result, the proposed hybrid method outperforms the other models for rockburst prediction in terms of the prediction accuracy and the generalization capability.