Real-time path planning is a critical task in robotics applications, this research paper delves into the optimization of Abrasive Water Jet Machining (AWJM) parameters for maximizing Material Removal Rate (MRR) and Depth of Cut (DOC). Water pressure, abrasive flow rate, traverse speed, and standoff distance are considered in this work as control parameters. The design of the experiment is chosen to be an L27 orthogonal array with 27 runs of experimental data gathering on MRR and DOC. Statistical models are developed to relate the control parameters to the objective functions of ln(MRR) and ln(DOC). The models are used to predict optimal working conditions for the AWJM process. The accuracy of the models is tested against experimental results, revealing a Mean Absolute Percentage Error (MAPE) of 4.83% for MRR and 3.64% for DOC. Subsequently, the Firefly Algorithm and Cuckoo Search are employed for optimization. The optimal solutions obtained through these algorithms are compared, showcasing the effectiveness of each in achieving enhanced machining outcomes. The research concludes with a detailed analysis of the optimal parameters and their impact on AWJM performance, offering valuable insights for practitioners and researchers in the field. The findings highlight the potential of metaheuristic algorithms in optimizing complex machining processes, contributing to the ongoing evolution of advanced manufacturing technologies.