<p>Accurately predicting the peak shear strength (<i>PSS</i>) of rock joints is of great significance for rock engineering and mining engineering. In this study, the whale optimization algorithm (WOA) and the sparrow search algorithm (SSA) were used to optimize the hyperparameters of the extreme gradient boosting (XGBoost) model, and two hybrid models, WOA-XGBoost and SSA-XGBoost, were established for predicting <i>PSS</i>. The performance of these two hybrid models was compared with XGBoost, gene expression programming (GEP), group method of data handling (GMDH), and two empirical models. The results show that the hybrid SSA-XGBoost model has the highest prediction accuracy, with its coefficient of determination (<i>R</i>²), root mean squared error (<i>RMSE</i>), mean absolute error (<i>MAE</i>), Willmott's Index of Agreement (<i>WIA</i>), and Legates-McCabe's Index (<i>LM</i>) being 0.9906, 0.2861 MPa, 0.1312 MPa, 0.9994, and 0.9591, respectively. Additionally, the SHapley additive explanations (SHAP) method was used to analyze the importance of each parameter on <i>PSS</i>.</p>