This paper presents two novel hybrid metaheuristic algorithms (CoChi-1) and (CoChi-2) that combines the Coyote Optimization Algorithm (COA) and the Chimp Optimization Algorithm (ChOA) to address complex optimization challenges. Both hybrid algorithms harness the exploration–exploitation balance of COA and the social intelligence-driven search mechanisms of ChOA in a different fashion, resulting in enhanced convergence speed and improved solution accuracy. The proposed approach has been evaluated on a comprehensive suite of 10 benchmark functions. Empirical results demonstrate significant performance improvements of the hybrid Algorithm over other individual algorithms and other state-of-the-art metaheuristics. Statistical and graphical analysis confirms the efficiency of the hybrid algorithm in some of the benchmark functions in solving optimization problems efficently.