Blasting of rocks has intrinsic environmental impacts such as ground vibration, which can interfere with the safety of lives and property. Hence, accurate prediction of the environmental impacts of blasting is imperative as the empirical models are not accurate as evident in the literature. Therefore, there is need to consider some robust predictive models for accurate prediction results. Gene expression programming (GEP), adaptive neuro-fuzzy inference system (ANFIS), and sine cosine algorithm optimized artificial neural network (SCA-ANN) models are proposed for predicting the blast-initiated ground vibration in five granite quarries. The input parameters into the models are the distance from the point of blasting to the point of measurement (D), the weight of charge per delay (W), rock density (ρ), and the Schmidt rebound hardness (SRH) value while peak particle velocity (PPV) is the targeted output. 100 datasets were used in developing the proposed models. The performance of the proposed models was examined using the coefficient of determination (R²) and error analysis. The R² values obtained for the GEP, ANFIS, and SCA-ANN models are 0.989, 0.997, and 0.999, respectively, while their errors are close to zero. The proposed models are compared with an empirical model and are found to outperform the empirical model.