Machine Learning in Intelligent Networks: Architectures, Techniques, and Use Cases

Integrating machine learning (ML) into intelligent networks (INs) has redefined the capabilities of modern communication systems by enabling real-time decision-making, adaptive optimization, and enhanced security. The present survey systematically analyzes state-of-the-art ML techniques deployed across diverse IN architectures, including centralized, decentralized, and hybrid frameworks. It delves into key ML methodologies—supervised, unsupervised, reinforcement, and deep learning (DL)—and highlights their transformative impact on network operations, such as resource allocation, fault management, and traffic optimization. Moreover, the survey emphasizes practical applications, showcasing the role of ML in enhancing performance across domains like telecommunication networks, smart cities, healthcare, and industrial automation. By consolidating fragmented research, this survey presents a unified perspective that bridges theoretical advancements with real-world implementations. The insights offered serve as a comprehensive reference for understanding how ML propels INs toward achieving unparalleled efficiency, reliability, and scalability levels. This work stands out by addressing implementation challenges, synergies across applications, and actionable strategies for leveraging ML in complex network environments.