Optimization algorithms play a crucial role in solving complex numerical problems across diverse domains. This paper presents a hybrid Grey Wolf Optimizer (GWO) and Harris Hawks Optimization (HHO) algorithm, designed to improve solution accuracy and convergence efficiency. The proposed hybrid approach leverages GWO’s structured leadership-based exploration with HHO’s dynamic and adaptive hunting strategies, ensuring a balanced trade- off between exploration and exploitation. The performance of the hybrid GWO-HHO algorithm is evaluated on twenty-three benchmark functions, and its results are compared with the original GWO. It is observed that the proposed hybrid approach achieves higher accuracy and improved optimization efficiency. In this paper GWO algorithm is combined with HHO algorithm for numerical optimization