For hydraulic tunnelling in rheological soft rocks, time-dependent support forces are likely to occur at the rock-support interface due to the interactions between the surrounding rocks and the support structures. A better understanding of the mechanism is crucial for the design of lining structures and the stability analysis of surrounding rocks. Numerical simulations of viscoelastic-plastic problems are time-consuming due to the large consumption of computational iterations and pre-/post- processing; meanwhile, the analytical approach needs complex formula derivation and program implementation. Therefore, both methods are far from satisfaction when it comes to engineering applications. In this study, a big group of datasets are obtained based on the viscoelastic-plastic analytical model for supported tunnels, with six different key parameters of material properties and geometry information of the surrounding rocks and support structures as the feature parameters. Subsequently, a data-driven model for predicting steady-state support forces is developed by LightGBM machine learning method. The results indicate that the model demonstrates stable predictive performance on the test set, with calibration determination coefficients for both the training and test sets exceeding 0.9, and mean absolute percentage errors below 4.2%, significantly outperforming other machine learning algorithms such as XGBoost and SVR. Finally, SHAP analysis is applied to assess the relationship between input features and predicted results, further enhancing the model’s interpretability and providing deeper insights into feature contributions. In summary, the developed data-driven model can rapidly predict steady-state support forces, and it can be further used in the design of support structures and other works of inverse analyses.