Intelligent Network Management Systems: A Comprehensive

Review of Artificial Intelligence, Machine Learning, and

Metaheuristic Optimization Approaches

Amir Ibrahim Neamatallah1,* Raneem Magdy Elagmy1 Ahmed Mohamed Yousry Ibrahim1

Mohamed Ahmed Ibrahim Ali1

1 Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt

Received: June 12, 2026 Revised: August 06, 2026 Accepted: September 05, 2026 ⋆ Corresponding author

ABSTRACT

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.

Keywords: Network Management System Artificial intelligence Machine learning Network automation Network

telemetry Software-defined networking Metaheuristic optimization

1. INTRODUCTION

The continuing evolution of communication infrastructure

has substantially increased the complexity associated with

monitoring, configuring, protecting, optimizing, and maintaining

modern networks. Earlier network environments were

commonly composed of comparatively stable device populations,

fixed topologies, limited service diversity, and management

procedures dominated by command-line configuration

and manually defined thresholds. Contemporary environments

differ markedly from this operational model. Enterprise

networks, cloud infrastructures, edge platforms, Internet

of Things deployments, wireless systems, virtualized services,

and software-defined networks can contain large numbers of

heterogeneous devices and logical functions whose states

change continuously. Traffic patterns fluctuate according to