Acid Mine Drainage (AMD) exists as a phenomenon that involves the release of acidic water and metal conjugates, in and around mines, degrading the surrounding water environment. A real-time mining effluent is treated using low-cost adsorption technology using Combined Vegetable Waste Carbon (CVWC) as sorbent. Batch sorption was reviewed to know the effect of process factors on the removal of Cadmium (Cd), Zinc (Zn), and Iron (Fe). A two-level CCD (Central Composite Design) with three factors was adopted in the optimization of process factors. Also, the same factors were considered to review the ANNs (Artificial Neural Networks), model. A comparative statistical analysis was performed for the experimental data based on RMSE and R² values in both RSM (Response Surface Methodology) and ANNs models. This study revealed that the ANNs model was well fit compared to RSM and this would probably reduce the experimental trials thereby reducing cumbersome calculations.