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The present study aims to compare the performance of two machine learning techniques that can unveil the relationship between the input and target variables and predict the ground vibration (peak particle velocity, PPV) due to quarry blasting. To this end, a Random Forest (RF) model and a Bayesian Network (BN) model were developed. Before developing these models, and in order to illustrate the necessity of proposing new intelligent systems, a new empirical equation is proposed, using maximum charge per delay and distance from the blast-face. The results confirm that there is indeed a need to develop intelligent systems with more input parameters. Thus, a Feature Selection (FS) model is applied to decrease the dimensionality of data and remove the irrelevant data. The outputs of this technique set five parameters, hole depth, power factor, stemming, maximum charge per delay and distance from the blast-face, as the most important model inputs necessary to predict PPV. After constructing FS-BN and FS-RF models and comparing them under different conditions (i.e., computational cost, accuracy and robustness), it is found that the developed FS-RF model can be introduced as a new model in the field of blasting environmental issues. The accuracy level of the FS-RF model is quite high; 92.95% and 90.32% for the train and test stages, respectively, while 92.95% and 87.09% accuracy is calculated for train and test of the FS-BN model. Thus, both developed hybrid models can effectively unveil the relationships between the input and target variables. • We measured the most effective parameters on peak particle velocity induced by blasting. • A new peak particle velocity empirical equation was proposed and compared with previous equations. • A feature selection model was applied to remove the irrelevant predictors. • Two hybrid models were developed to predict peak particle velocity. • A comparison between these models was made and the best among them was chosen. [ABSTRACT FROM AUTHOR]