The prediction of air and ground vibrations is an important problem in rock blasting activities. The aim of this study is to evaluate the prediction of ground and air vibrations by using intelligent networks and traditional regression model. So, fuzzy logic and artificial neural network (ANN) models have been constructed to predict peak particle velocity and air overpressure induced by blasting in Assiut Cement Company. For this purpose, the peak particle velocity, air vibrations, and charge weight per delay were recorded for 136 blast events at various distances and used for the training of the predictor models. About new 26 data sets have been used to test and validate the models. The performance, validity and capability of these models to predict were proved to be successful by statistical performance indices. These indices are variance-accounted for (VAF) and root mean square error (RMSE). The results from these models asserted that, intelligent networks technologies can be precisely and effectively used for predicting the air and ground vibrations in comparison with traditional regression analysis. Also, the comparison indicated that the fuzzy logic model exhibited slightly better prediction performance and generalization than the artificial neural network in ground and air vibration prediction.