The intricacy of decision variables, multiple objectives, and nonlinear restrictions make it difficult to find suitable solutions for mechanical design problems. An alternative approach to these difficult challenges, the Grey Wolf Optimizer (GWO) is recognized for its ease of use, flexibility, scalability, and unique balance between exploration and exploitation. Like every stochastic approach, GWO has drawbacks, though, and numerous enhanced variants have been put up to overcome them. The GWO algorithm and its variants are examined in this investigation. It conducts an experimental comparison of the original approach and its two variations. It examines how the approaches behave with various combinations of parameters. Five mechanical design problems are used to test the algorithms' effectiveness utilizing statistical analysis and search performance. In the literature, the performance of alternative approaches is also contrasted with the ideal outcomes.