AI-Driven Network Management and Optimization

This comprehensive literature analysis, meticulously synthesizes current knowledge and advancements in AI-driven network management and optimization. The study systematically reviews a broad spectrum of scholarly works published predominantly within the last decade, employing a rigorous methodology that involved a multi-stage process of systematic search, selection, and thematic analysis of peer-reviewed articles, conference papers, and authoritative reports. The primary objective is to elucidate how artificial intelligence, encompassing a diverse array of techniques such as machine learning (e.g., supervised, unsupervised), deep learning (e.g., CNNs, RNNs), and reinforcement learning, is profoundly transforming traditional network paradigms. This transformation is evident in its capacity to enhance operational efficiency, automate complex management tasks, and provide predictive capabilities for improved network performance, bolster cybersecurity measures, and optimize resource allocation across various network domains, including 5G cellular networks, Internet of Things (IoT) ecosystems, cloud-native infrastructures, and software-defined networks (SDN). Key findings reveal that AI is unequivocally a critical enabler for sophisticated network automation, offering substantial improvements in proactive problem identification, dynamic resource provisioning, intelligent traffic engineering, distributed anomaly detection, and adaptive quality of service management. The analysis highlights prominent trends such as the increasing adoption of deep reinforcement learning for dynamic routing and congestion control, federated learning for distributed anomaly detection, and the burgeoning importance of explainable AI (XAI) for enhancing the trustworthiness and transparency of AI-driven decisions. However, the review also underscores significant challenges impeding widespread adoption and full realization of AI's potential, including concerns related to data privacy and security, the inherent complexity and computational demands of advanced AI models, the critical need for algorithmic interpretability, and intricate ethical considerations surrounding autonomous decision-making in critical network infrastructure. In conclusion, the seamless integration of AI into network operations represents a profound and inevitable paradigm shift, necessitating continuous research focused on developing more robust, scalable, secure, and inherently explainable AI models, alongside the formulation of practical deployment strategies and adaptable regulatory frameworks to fully realize the transformative potential of intelligent, self-optimizing networks.