AI and ML empowering 5G and shaping the 6G future: Models, metrics, architectures, and applications

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) technologies are becoming more important in wireless telecommunications networks, especially in the transition from 5G to 6G, a more advanced AI networking environment. While in 5G networks AI is used basically to get better performance from the individual tasks, in 6G, AI will be a model that is used at each layer of the system design-from the physical retransmission of the signals right through to the management of the services. The paper will examine the advanced AI technologies of Deep Learning, Reinforcement Learning, Generative Models, and Federated Learning, and their impact on core processes in the networking framework like beamforming, channel estimation, spectrum access, and anomaly detection which are evaluated against core metrics of accuracy, latency, power consumption, privacy, and comprehensibility. In the process of going beyond technical detail, the review situates AI-based wireless innovations in different fields including autonomous vehicles, telesurgery, industrial IoT, and smart cities. It also points out the persistent challenges, such as data scarcity, real-time inference, edge deployment, and ethical concerns, and presents some promising future research directions, including digital twins, AI–quantum convergence, and regulatory frameworks. This work presents a strategic roadmap to achieve scalable, secure, and intelligent 6G networks by providing a cross-layer and cross-domain synthesis.