The current research trend is the hybridization of several variations to further improve solution quality for practical and contemporary real-world applications within the domain of global optimization problems. This study presents a novel strategy that combines the Grey Wolf Optimizer (GWO), Sine Cosine Algorithm (SCA) and Particle Swarm Optimization (PSO) algorithm. Hybrid GWOSCAPSO is a combination of Grey Wolf Optimizer, Sine Cosine Algorithm (SCA) and Particle Swarm Optimization utilized to enhance GWO’s exploration and exploitation phases in unpredictable environments. The formulas used to update SCA’s location are utilized to improve the directional movement and the alpha wolves speed, while PSO is implemented to update the particle placement of the wolves belonging to the beta and delta ranks. The efficacy of GWOSCAPSO in addressing optimization difficulties is assessed through its application to a combined total of 23 standardized classical benchmark test problems. The proposed hybrid GWOSCAPSO technique is compared to those obtained employing several metaheuristic methodologies, including Ant Lion Optimizer (ALO), Whale Optimization Algorithm (WOA), Moth-Flame Optimization (MFO), GWO, SCA and PSO. The empirical evidence obtained from numerical and statistical experiments gives substantial evidence that the hybrid approach that was presented is helpful in resolving the benchmark concerns while significantly outperforming its competitors on a vast range of the problems.