The PID controller is a crucial element in numerous engineering applications. However, a significant challenge with PID lies in selecting optimal parameter values. Conventional methods need extra tunning and may not yield the best per- formance. In this study, a recently introduced metaheuristic algorithm, Geometric Mean Optimizer (GMO), is employed to identify the most suitable PID parameter values. In conventional methods, a fixed empirical equations are applied to select pa- rameter values of PID. In GMO, there is a wide search space to select the optimal parameter values of PID based on an objective function. The objective function that the GMO seeks to minimize is the Integral of Absolute Error (IAE). GMO is chosen for its effectiveness in balancing exploration and exploitation of the search space, as well as its robustness and scalability. GMO is tested in the context of optimizing PID parameters for an engineering application: DC motor regulations. The results demonstrated GMO’s superiority over comparable algorithms.