Metaheuristic algorithms have been categorized as vehicles for solving problems hard to solve by conventional methods of optimization. These generational algorithms, borrowed from systems like genetic evolution, swarm intelligence, and annealing, provide formidable approaches to searching huge solution spaces and finding the optimal solutions in engineering and manufacturing. Introduced within this paper, the five fundamental metaheuristic algorithms the genetic algorithm, particle swarm optimization, simulated annealing, ant colony optimization, and whale optimization algorithm have been considered and compared. Researchers have carried out a few numbers of studies to compare the efficiency of different meta-heuristic optimization algorithms. This study’s goal is to evaluate the algorithms in identifying the friction stir Welding best parameters. This thesis optimizes Friction Stir Welding (FSW) parameters to enhance mechanical properties of aluminum alloys using meta-heuristic algorithms. It starts with Design of Experiments (DOE) in Minitab, focusing on Signal-to-Noise (S/N) ratios and Grey Relational Analysis (GRA) to calculate Grey Relational Coefficients (GRC) and Grades (GRG). A regression model is developed from GRG data, with ANOVA validating parameter significance. This model is then optimized in MATLAB using algorithms like GA, ACO, PSO, SA, and WOA. The study compares these algorithms’ effectiveness in improving mechanical properties.In AA5754, they recorded a great enhancement in tensile strength by 35.80% and 35.79% compared with Ant Colony Optimization, while enhancement on Genetic Algorithm and Simulated Anealing was 22.56% and 34.23%, respectively. For tensile strength, Whale Optimization Anealinng and Particle Swarm Optimization recorded an astounding 45% increment as compared to Simulated Anealing 35% and Ant Colony Optimization only 10% in AA6082. In terms of ultimate tensile strength, Whale Optimization Anealing and Particle Swarm Optimization performed the best, with increases of 44.6% for both AA6082 and AA5456, followed by a 30% increase in Genetic Anealing and an 11.9% increase in Simulated Anealing. Whale Optimization Algorizms and Particles Swarm Optimization also provided the best solution for hardness in AA5754, and it was enhanced by 56% while Genetic Algorithm and Simulated Anealing enhanced it by 51% and 47%, respectively