<p>Constraint optimization problems (COPs) are fundamental in various real-world applications, spanning engineering, economics, and artificial intelligence. Traditional methods for solving COPs often struggle with balancing objective function optimization and constraint satisfaction, especially in complex, high-dimensional, and discontinuous search spaces. This paper introduces RECHT (Ranking-based Ensemble Constraint Handling Technique), an advanced constraint-handling approach that integrates penalty functions, feasibility rules, stochastic ranking, and the e-constrained method within a ranking-based ensemble framework. RECHT enhances evolutionary algorithms (EAs) by dynamically adjusting constraint-handling strategies, improving convergence rates while minimizing constraint violations. The proposed technique is evaluated on benchmark constrained optimization problems from CEC competitions, demonstrating superior performance compared to standalone EAs. Experimental results show that RECHT effectively reduces function evaluations, achieves higher-quality solutions, and maintains robust performance across diverse problem structures. The findings highlight the significance of ensemble-based constraint-handling techniques in advancing optimization methodologies.</p>