aspg-41-4505-1792223091 20261017074451 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Metaheuristic Optimization Review MOR 3066-280X 2026 7 2 Amir Neamatallah Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt Raneem Elagmy Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt Ahmed Ibrahim Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt Mohamed Ali Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt Modern communication networks have become increasingly heterogeneous, programmable, distributed, and servicedriven, creating management requirements that exceed the capabilities of purely manual or static rule-based operation. This review examines the evolution of intelligent Network Management Systems (NMSs) through the integration of machine learning, deep learning, predictive analytics, feature selection, metaheuristic optimization, network telemetry, software-defined control, and automated decision mechanisms. The reviewed methodological directions support traffic and performance prediction, anomaly and intrusion detection, fault localization, configuration optimization, resource allocation, routing assistance, quality-of-service management, capacity planning, and closedloop remediation. Intelligent approaches can strengthen situational awareness and reduce the time required to identify, diagnose, and respond to rapidly changing network conditions, while metaheuristic algorithms provide flexible mechanisms for feature-space reduction, model calibration, multi-objective resource optimization, and search within large configuration spaces. Practical deployment nevertheless remains constrained by telemetry quality, concept drift, computational overhead, interoperability, multi-vendor heterogeneity, security, privacy, explainability, scalability, and the risk of unsafe automated actions. Future research should therefore prioritize trustworthy, policy-aware, scalable, secure, and adaptive NMS architectures capable of combining continuous observability with verifiable closed-loop automation across enterprise, cloud, edge, Internet of Things, and multi-domain network environments. 2026 51 72 4505 https://creativecommons.org/licenses/by/4.0/ 10.54216/MOR.070204 https://americaspg.com/journal/41/article/4505 https://americaspg.com/storage/articles/manuscripts/01M2KA8SXEZM8B226CG7V28HD0.pdf