The exploitation of underground mineral deposits is dominantly accomplished using drilling and blasting. The large-scale blasting with higher explosive consumption is practiced to achieve the production targets. However, such large-scale blasting induces ground vibration, a hazard to structural safety and environmental nuisance. The magnitude of ground vibration can be reduced by devising a controlled blasting pattern. Such a pattern is devised by developing the ground vibration predictive models. Machine learning based on the Random Forest predictive model was developed in this study to predict the ground vibration induced by underground blasting. The number of trees in this predictive model influences prediction accuracy and computational time. Accordingly, it was optimally selected to maximize the R2 and minimize the RMSE values. Once the predictive model was developed, the predictions were made for the testing data set. The predictions were also made using various empirical relationships. The comparison of the predictive models shows that RF is the best predictive model with R2 and RMSE values of 0.94 and 0.438 mm/s, respectively. This is because the RF-based predictive models consider interdependency among the parameters, whereas the empirical relationships directly use the input parameters to predict ground vibration. Because of the better accuracy of the RF predictive model, it is being used for day-to-day ground vibration prediction at the experimental site.