Increasing energy efficiency in climate change mitigation has become essential due to continuous population growth and rising energy consumption. Energy-intensive industries, such as mining, have areas for improving energy consumption. Haulage systems have often been considered for optimization in mining operations. Therefore, this research aims to evaluate Semi-Mobile In-Pit Crushers and Conveyors (SMIPCC) to optimize loading and haulage systems and reduce energy consumption. For this purpose, two loading and hauling scenarios were considered: the first was based on discontinuous loading and haulage, and the second was based on semi-continuous loading and haulage using SMIPCC. First, two potential locations are assessed and investigated based on the ore deposit’s center of gravity. Also, eight potential sites for placing four crushers related to waste crushing are obtained. Next, the slope and distance within these potential locations are evaluated and compared. After this, the optimal location is selected based on minimizing slope and distance. The obtained slope and distance values for each crusher are then compared with the current values from the first scenario. Finally, XGBoost and Random Forest models were used to predict energy consumption in the initial locations. After comparing the models based on R² and MAPE criteria, the Random Forest model was the most accurate for predicting diesel fuel consumption with an R² of 0.88 and a MAPE of 5.7425, respectively. The results demonstrated that SMIPCC systems reduced truck energy consumption by 24%. In addition, a significant reduction in CO₂ emissions was observed, reaching approximately 25%. Such improvements indicate the reliability of SMIPCC systems in promoting reduced energy consumption and sustainable mining operations.