Unraveling the Nexus of ML and 6G: Challenges, Opportunities, and Future Directions

Abstract

Machine learning (ML) and sixth-generation (6G) wireless networks together present a revolutionary paradigm ready to meet hitherto unheard-of needs in connection, scalability, and intelligence. Including physical-layer signal processing, edge computing, resource optimisation, network slicing, and security, this study provides a thorough and cross-layer study of ML integration into 6G systems. Unlike previous works that concentrate on isolated use cases, this paper systematically classifies and evaluates developing techniques such as federated learning (FL), quantum machine learning (QML), swarm intelligence, and explainable AI within the framework of key 6G performance indicators including ultra-low latency, energy efficiency, and privacy. Presenting a thorough taxonomy and comparative benchmarking, we show the operational trade-offs of centralised, distributed, and meta-learning models. We also find important difficulties like model heterogeneity across ultra-dense networks, adversarial vulnerability in FL, and reconfigurable intelligent surfaces (RIS)-assisted privacy risks. Moreover, we describe open research areas and suggest a single orchestration design to support intelligent, scalable, and reliable 6G systems. Researchers and professionals aiming to create strong, ML-driven wireless infrastructures beyond 5G find basic reference in this work.