This paper presents a comprehensive review of metaheuristic algorithms across engineering disciplines over the period 2000-2025. We systematically analyze the literature on metaheuristic algorithms and their applications to structural design, power systems, control, manufacturing, aerospace, p. The review covers approximately 192 papers, providing a structured taxonomy of algorithms, applications, and evaluation methodologies. We identify key trends including the shift toward hybrid approaches, integration of Machine Learning (ML), and growing emphasis on explainability. The survey reveals that constraint handling, multi-objectivity, real-time, scalability, robust 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.