AI-Driven Innovations in Infrastructure Management with 6G Technology

The emergence of 6G networks brings in rigorous demands to enable next-generation applications such as holographic communication, XR, autonomous systems, and real-time automation. Conventional static management strategies are inadequate to fulfill these dynamic needs. This paper suggests AI-native infrastructure management based on hierarchical reinforcement learning, intent-based orchestration, and digital twin simulations. Global actor-critic models manage macro resource allocation, with edge-based DQNs providing real-time local optimization. Benchmarking with UCI Network Traffic and Gas Sensor Drift datasets shows a 34% latency decrease, 21% energy reduction, and better SLA satisfaction. These findings point to the framework's potential to be a robust, adaptive solution for intelligent 6G network management.