This study emphasizes the importance of numerical modeling in examining the load behavior inside laboratory-scale ball mills, focusing on the most critical parameters affecting the behavior of the slurry of copper ore, water, and grinding medium (steel balls). A Design of Experiments (DoE) approach was employed to plan the experiments, conducted using a test stand featuring a transparent wall for visual observations. High-speed camera recordings of the mill load were analyzed using computer vision algorithms, while additional measurements, such as rotational speed, torque, and power draw, were obtained through sensors. The milling efficiency was evaluated by comparing the grain sizes (d80) of the input material and product. The experiments were conducted with three different ball mill diameters, allowing the analysis of the impact of scale on grinding efficiency. This comprehensive approach distinguishes our study from previous research [1, 2], which only utilized one ball mill diameter. The experiments were replicated in a computational environment employing Discrete Element Method (DEM) and Computational Fluid Dynamics (CFD) simulations, emphasizing the importance of numerical modeling in understanding the complex dynamics of ball mills. The findings of this study contribute to the general significance of optimizing load behavior in ball mills, potentially improving copper ore grinding efficiency and reducing energy consumption. These insights may also be applicable to other mineral processing industries, demonstrating the value of numerical modeling in investigating the behavior of bulk materials.