This paper presents a comprehensive review of metaheuristic optimization algorithms across engineering and scientific domains over the period 2000-2025. We systematically analyze the literature on metaheuristic algorithms and their applications to engineering design, scheduling, Machine Learning (ML), signal processing, c. The review covers approximately 165 papers, providing a structured taxonomy of algorithms, applications, and evaluation methodologies. We identify key trends including the shift toward hybrid approaches, integration of ML, and growing emphasis on explainability. The survey reveals that parameter tuning, premature convergence, scalability, benchmark fairne remain significant open problems. We provide detailed analysis of evaluation protocols, benchmark suites, and statistical methodologies. Future research directions include hybrid algorithm design, quantum-inspired methods, and standardized benchmarking frameworks.