Electrical discharge machining (EDM), which has a number of distinctive characteristics, has established itself as one of the effective non-conventional machining techniques for producing complex profile geometries on a variety of advanced engineering materials to meet the needs of modern engineering industries. This study mainly focuses on recently developed preying behavior metaheuristic algorithms, namely, sailfish optimization algorithm, harris hawk optimizer, aquila optimization algorithm, bat optimizer, and grey wolf optimizer. The experimental dataset deals with the electrical discharge machining using Al-SiC metal matrix composite to investigate the three response parameters these are material removal rate, surface roughness, and overcut. Thirty-four experimental trials, based on the Box-Behnken design method are conducted by considering five process variables. The results of single and multi-objective optimization, are validated using Friedman's mean rank test, and evaluated in terms of convergence speed, quality of the solutions, and computing effort. The grey wolf optimizer is found the best optimization algorithm over other four algorithms. The percentage of improvement in optimal results for the grey wolf optimizer, in case of single objective optimization are 5.91%, 29.3%, and 26.01%, and for multi-objectives, achieve 29.41%, 29.76%, and 32.57% for material removal rate, surface roughness, and overcut respectively.