<p>Ball burnishing is a finishing process widely used to improve the surface integrity of metallic components through controlled plastic deformation. This study introduces a novel integrated approach combining experimental modeling and multi-objective optimization for the ball burnishing of AISI 1045 steel. The effects of three key parameters, burnishing force, spindle speed, and feed rate, were investigated with respect to two critical responses: surface roughness and hardness. Experimental trials were designed using Response Surface Methodology (RSM), enabling the development of reliable empirical models capturing both main effects and parameter interactions. Statistical analysis revealed that burnishing force is the most influential parameter, followed by spindle speed and feed rate. The RSM models were then embedded within a multi-objective optimization framework based on the NSGA-II genetic algorithm, generating Pareto-optimal solutions that provide robust trade-offs between surface roughness and hardness. The optimal solution simultaneously achieved a 92.5% improvement in surface roughness and a 13.2% increase in hardness, reaching unprecedented levels of performance. These findings provide valuable insights into the burnishing mechanisms of AISI 1045 steel and offer practical guidelines for selecting optimal process parameters in industrial applications. Moreover, this methodology shows potential for adaptation to other steel grades and metallic alloys, offering a transferable framework for industrial process optimization.</p>