To accurately predict the peak particle velocity (PPV) and effectively reduce the hazards of blasting vibration, a prediction model was built by BP neural network based on the blasting project of Xingguang No.1 open-pit mine. Seven influencing factors as core distance, plugging length, minimum resistance line, explosives unit consumption, maximum single-hole charge, total extension time, and maximum single-delay charge, were selected as input variables, and the correlation between each factor and PPV was evaluated by using the grey correlation analysis method. The Sparrow Search Algorithm (SSA) optimized the BP neural network to predict the three-way peak vibration velocity. By comparing and analyzing the prediction results of the BP neural network model, the average errors of the prediction results of the SSA-BP neural network model were 6.08%, 7.34%, and 1.91%, respectively, and that of the prediction results of the BP neural network model was 22.19%,54.01%, and 25.29%, respectively. The results show that the SSA-BP neural network model comprehensively considers the influence of multiple blasting design parameters on the peak vibration velocity. The sparrow search optimization algorithm can effectively solve the problem of the traditional BP neural network model, which quickly falls into the local optimum. The prediction results are more accurate, and the vibration velocity monitoring value is more consistent with smaller errors. Meanwhile, it can significantly shorten the learning and training time of the sample data to speed up the convergence speed of BP. Additionally, it can also significantly shorten the training time of sample data and accelerate the convergence speed of the BP neural network prediction model.