Abstract: In order to improve the prediction accuracy of peak particle velocity during open-pit blasting operations， which is insufficient when using traditional empirical formulas and single bionic algorithms， the Grey Wolf Algorithm (GWO) is introduced to optimize 2 hyper parameters， the number and depth of decision trees in the Random Forest Algorithm (RF). This successfully constructs the GWO-RF blasting vibration velocity prediction model. By combining 69 sets of blasting monitoring data from a blasting project， input parameters such as blast hole distance， maximum interval charge， total charge， millisecond delay， number of blast holes， hole spacing， depth， and row spacing are used to compare the prediction of peak vibration velocity using the GWO-RF model and the RF model. The results show that the GWO-RF combined algorithm can consider more practical factors affecting blasting vibration velocity and improves the error rate by 37.83 percentage points compared to the RF； the prediction accuracy of blasting vibration velocity using the GWO-RF combined algorithm reaches 97.72 %. This indicates that the GWO successfully optimizes the 2 hyper parameters of decision trees in the RF model and demonstrates that the GWO-RF combined algorithm can be used for accurate prediction of blasting vibration velocity in open-pit mining.