This study explores the application of the Multi-objective Cuckoo Search Algorithm (MOCS) for optimizing cutting parameters in machining operations. The primary objective is to simultaneously minimize surface roughness and three vibration components—tangential, radial, and axial—by considering the cutting speed, feed rate, and cutting edge angle as variables. The research employs a full factorial design to collect experimental data, which are analyzed using Response Surface Methodology (RSM) and Analysis of Variance (ANOVA). The MOCS algorithm was implemented to generate Pareto optimal solutions, representing trade-offs between conflicting objectives. Additionally, the augmented epsilon-constraint (AUGMENCON) method was introduced to validate the results. The study reveals that the optimal configuration achieves a balance between surface roughness and vibration reduction, with Pareto solutions providing decision-makers diverse options to enhance machining efficiency. The results indicate that MOCS offers robust performance, with findings closely aligning with those obtained from the AUGMENCON method, demonstrating its effectiveness in optimizing complex machining processes.