In deep underground mining, rockburst is taken into account as an uncertainty risk with many adverse effects (i.e., human, equipment, tunnel/underground mine face and extraction periods). Due to its uncertainty characteristics, accurate prediction and classification of rockburst susceptibility are challenging, and previous results are limited. Therefore, this study proposed a robust hybrid computational model based on gene expression programming (GEP) and particle swarm optimization (PSO), called GEP-PSO, to predict and classify rockburst potential in deep openings with improved accuracy. A different number of genes (from 1 to 4) and linking functions (e.g., addition, extraction, multiplication and division) in the GEP model were also evaluated for development of the GEP-PSO models. Geotechnical and constructive factors of 246 rockburst events were collected and used to develop the GEP-PSO models in terms of rockburst classification. Subsequently, a robust technique to handle missing values of the dataset was applied to improve the dataset's attributes. The last step in the data processing stage is the feature selection to determine potential input parameters using a correlation matrix. Finally, 13 hybrid GEP-PSO models were developed with varying accuracies. The findings indicated that the GEP-PSO model with three genes in the structure of GEP and the multiplication linking function provided the highest accuracy (i.e., 80.49%). The obtained results of the best GEP-PSO model were then compared with a variety of previous models developed by previous researchers based on the same dataset. The comparison results also showed that the selected GEP-PSO model results outperform those of previous models. In other words, the accuracy of the proposed GEP-PSO model was improved significantly in terms of prediction and classification of rockburst susceptibility. It can be considered widely applied in deep openings aiming to predict and evaluate the rockburst susceptibility accurately.