<p>This study integrates machine learning fundamentals with metaheuristic optimizers to introduce a novel optimization approach that builds upon existing research. The proposed strategy employs the Fuzzy C-means clustering algorithm to guide local search via dedicated optimization agents, and Q-learning to select the most suitable optimization algorithm at each iteration. Three recent metaheuristic optimizers—the Manta Ray Foraging Algorithm, African Vulture Algorithm, and Harris Hawks Optimization—are enhanced using these intelligent learning techniques to improve solution accuracy and robustness. In addition, a novel optimization strategy based on integrating search equations of Dynamic Oppositional-based Learning mechanisms and the Generalized Quadratic Interpolation method is proposed and embedded into the developed optimization framework, achieving higher efficiency in improving the exploration and exploitation balance of the algorithm. Hyperdimensional standard benchmark problems of varying complexity have been solved using the proposed hybrid method. The new method is also evaluated using challenging test functions from the CEC 2013 competition, which span multidimensional, 30D, 50D, and 100D unimodal, multimodal, and composite benchmarks. Compared with state-of-the-art optimizers, the proposed algorithm outperforms them in most scenarios, underscoring its effectiveness in tackling complex problems. Moreover, the study addresses a critical yet often overlooked aspect of shell-and-tube heat exchanger design: the impact of in-tube refrigerants on overall efficiency and cost. By assessing more than 50 refrigerants with varying thermophysical properties, the research identifies optimal configurations to minimize total expenditure, finding that systems operating with NH₃ and R161 yield the lowest overall costs compared with alternative configurations.</p>