This work introduces a unique hybrid optimization algorithm that integrates the Learner Phase of the Teaching–Learning-Based Optimization (TLBO) algorithm with the Enhanced Jaya Algorithm (EJAYA). This hybrid methodology seeks to equilibrate exploitation and exploration during the optimization procedure. To assess the performance of the proposed hybrid algorithm, we implemented it to Twenty-three benchmark functions. Our evaluations included five popular metaheuristic algorithms: TLBO, EJAYA, JAYA, Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). We ran each algorithm twenty times, for one thousand iterations. The four measures used to assess the performance were rank, standard deviation, best result, and mean. Based on the outcomes, it is evident that the suggested algorithm outperformed the alternatives. The results show that the hybrid algorithm is the best option available and can successfully replace the existing optimization techniques.