In view of the problem that it is difficult to determine the optimization parameters of rock material yards and mine blasting in water conservancy projects, the traditional methods mainly rely on empirical formulas and experimental adjustments. To solve this issue, this study proposes a parameter optimization method that integrates an improved whale optimization algorithm (GWOA) with back propagation (BP) neural network. A GWOA-BP model was developed with optimization objectives focusing on explosive consumption per unit, borehole spacing, and row spacing. Comparative analysis was performed using measured blasting data of a certain project in Yunnan Province. The results indicate that the GWOA-BP model outperforms the conventional BP model in terms of prediction accuracy and stability, with error metrics significantly reduced. This research offers a novel technical approach for optimizing blasting parameters in simliar projects.