Reliable prediction of blast-induced rock fragmentation is essential for optimizing blasting performance, reducing operational costs, and improving the efficiency of downstream mining operations. This study develops and evaluates hybrid machine learning models by integrating Backpropagation Neural Network (BPNN) and Support Vector Regression (SVR) with three optimization algorithms, namely the Zebra Optimization Algorithm (ZOA), Grey Wolf Optimizer (GWO), and Bayesian Optimization (BO). The resulting hybrid models, ZOA-BPNN, GWO-BPNN, BO-BPNN, ZOA-SVR, GWO-SVR, and BO-SVR, were compared with their standalone counterparts using the Mean Absolute Error (MAE), Mean Squared Error (MSE), Correlation Coefficient (R), and Coefficient of Determination (R2). Among the developed models, ZOA-BPNN achieved the highest predictive performance, with a validation MAE of 0.021, MSE of 0.046, R of 0.992, and R2 of 0.984. Model interpretation using SHapley Additive exPlanations (SHAP) identified powder factor as the most influential variable governing fragment size, followed by charge per hole, charge length, and bench height, thereby providing valuable insights for blast design optimization. Overall, the results demonstrate that integrating metaheuristic optimization with machine learning substantially enhances prediction accuracy and robustness, with ZOA-BPNN emerging as the most effective framework for intelligent prediction and decision support in surface mine blasting.
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