WEDM is an intricate process whereby improper selection of machine parameters often leads to undesirable performances. Therefore, the extraction of optimal machining parameters is pivotal for achieving better performances in WEDM. Metaheuristic optimizers have gained immense popularity due to their capability of providing global optimal solutions. The application of recently reported metaheuristic optimizers in non-traditional machining processes is rarely being explored. In light of the above, the current paper examines the use of six recently reported metaheuristic optimizers, namely the ant lion optimization (ALO), chimp optimization algorithm (ChoA), moth flame optimization (MFO), spotted hyena optimization (SHO), Harris Hawk optimization algorithm (HHO), Marine predator algorithm (MPA) to optimize WEDM performances in three WEDM processes. Particle swarm optimization (PSO) and Teaching learning-based optimization (TLBO), two well-known existing optimization approaches, are also included in this study to enable a reasonable comparison of the algorithms' performance. The algorithms are compared with parameters such as the quality of optimal solutions, convergence behavior, and average computational time. HHO algorithm is found to be robust amongst the eight competitors in terms of culminating the global optimal solution and propensity to quickly converge to the global optimal solution which corroborates the high exploration and exploitation capability of the algorithm. Therefore, HHO optimizer can be exploited in future to determine the optimal operating conditions for other manufacturing processes.