Metaheuristic Optimization Review MOR 3066-280X 10.54216/MOR https://www.americaspg.com/journals/show/4207 2024 2024 Department of Electronics and Communications Engineering, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt Ahmed Ahmed Department of Electronics and Communications Engineering, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt Hala B. Nafea Department of Electronics and Communications Engineering, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt; Faculty of Artificial Intelligence and Informatics, Horus University, New Damietta, 34517, Egypt Hossam El-Din Moustafa Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt; Applied Science Research Center, Applied Science Private University, Amman, Jordan El-Sayed M. El El-Kenawy This comprehensive review examines the current state of artificial intelligence and computational optimization techniques applied to earthquake engineering challenges. The paper systematically analyzes recent advances across three primary domains: machine learning (ML), deep learning (DL), and optimization methods, each contributing distinct capabilities to seismic hazard mitigation. Through an extensive analysis of peer-reviewed studies, this review synthesizes methodologies employed in earthquake prediction, early warning systems, structural damage assessment, emergency response optimization, and seismic hazard analysis. Machine learning approaches have demonstrated significant effectiveness in liquefaction prediction, slope displacement analysis, and seismic event classification, with models such as XG Boost and Random Forest achieving high predictive accuracy. Deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based models, have revolutionized real-time earthquake detection, P-wave recognition, and landslide susceptibility mapping, with several models achieving accuracy rates exceeding 90%. Optimization techniques, particularly metaheuristic algorithms like Particle Swarm Optimization (PSO) and Gray Wolf Optimizer (GWO), have proven valuable for emergency logistics, shelter allocation, and structural design optimization. The review reveals current trends toward hybrid frameworks integrating multiple computational approaches, enhanced model interpretability, and real-time implementation capabilities. Future research directions emphasize the development of uncertainty-aware models, scalable frameworks for global application, and integration of social and economic factors in disaster preparedness strategies. This review provides researchers and practitioners with a structured understanding of computational methodologies in earthquake engineering and identifies critical gaps requiring further investigation. 2026 2026 53 91 10.54216/MOR.050204 https://www.americaspg.com/articleinfo/41/show/4207