The rapid growth of Optimization and Metaheuristics research has led to both innovation and oversaturation, making algorithm selection increasingly challenging. While most studies assess algorithms using the Synthetic Benchmark Problems (SBPs) often oversimplify real-world complexities, potentially misrepresenting performance. To address this, we evaluate 25 metaheuristic algorithms across 50 Real-World Constrained Multi-objective Optimization Problems (RWCMOPs), using performance metrics such as Hypervolume (HV), Feasibility Rate (FR), and Wall-Clock Time. Our findings show that classical algorithms like GDE3 and NSGA-II often outperform newer methods in practical scenarios. Notably, constraint structure and feasibility difficulty emerge as more influential factors than problem dimensionality. This study offers key insights into algorithmic robustness and underscores the importance of real-world testing to guide more effective algorithm design beyond synthetic settings.