Metaheuristic Optimization Review MOR 3066-280X 10.54216/MOR https://www.americaspg.com/journals/show/4202 2024 2024 Engineering School of Digital Technologies, Yugra State University, Khanty Mansiysk, 628012, Russia Mostafa Mostafa Monkeypox (mpox) has emerged as a significant re-emerging zoonotic threat, with the 2022–2023 global outbreak underscoring the need for rapid detection, genomic monitoring, and predictive intervention strategies. This work presents a structured synthesis of three major research domains: (1) detection and classification, encompassing convolutional neural networks (CNNs), transformer-based architectures, capsule networks, transfer learning, feature selection, ensemble methods, and explainability tools applied to lesion images for accurate diagnosis; (2) genomics, prediction, and reviews, covering time series modeling of viral genome mutations using long short-term memory (LSTM) networks, phylogenetic analysis, mutation hotspot identification, and critical reviews of AI-based diagnostic methods and metaheuristic optimization strategies; and (3) intervention support, focusing on outbreak forecasting, gradient boosting risk models, and non-stationary LSTM frameworks for scenario planning and resource allocation. Across categories, recurring challenges include limited and imbalanced datasets, inconsistent reporting, and the gap between algorithmic accuracy and clinical or operational integration. This synthesis highlights methodological trends, identifies limitations, and outlines research priorities: developing multicenter datasets, leveraging multimodal integration of phenotype and genotype, adopting federated and semi-supervised learning to address data scarcity, and coupling predictive models with operational feasibility assessments. By linking technical innovation with practical outbreak management needs, this work bridges the gap between computational research and public health application, offering a roadmap for mpox preparedness and control in both endemic and non-endemic regions. 2026 2026 83 103 10.54216/MOR.050105 https://www.americaspg.com/articleinfo/41/show/4202