(untitled) (International Journal of Artificial Intelligence Engineering and Systems, 2026)
The transition to 5G-Advanced and beyond represents a fundamental shift in wireless system design, demanding infrastructures that simultaneously support heterogeneous service requirements, dynamic resource allocation, and stringent energy efficiency targets. Network slicing has emerged as a cornerstone architectural principle, enabling multiple logical networks to coexist on a shared physical substrate. However, the operational complexity of orchestrating slices with diverse quality-of-service profiles under time-varying traffic and channel conditions pushes traditional heuristic and optimization-based resource scheduling to their limits. Artificial intelligence, particularly deep reinforcement learning, offers a transformative pathway toward adaptive, self-optimizing slice management that can jointly pursue energy conservation and performance guarantees. This paper provides a comprehensive systems-level analysis of AI-enabled energy-efficient network slicing and resource scheduling in 5G-Advanced wireless systems. It examines the architectural enablers, the integration of machine learning into slice orchestration, and the structural trade-offs between efficiency, fairness, robustness, and sustainability. Discussions span from centralized and distributed learning paradigms to model interpretability, deployment feasibility, and cross-domain governance. The analysis emphasizes infrastructure-scale implications, including life-cycle energy impacts, resource isolation, and the alignment of AI-driven decisions with regulatory and standardization frameworks. By synthesizing perspectives from telecommunications, systems engineering, and socio-technical governance, the paper articulates a forward-looking research agenda that prioritizes not only algorithmic innovation but also systemic resilience, policy coherence, and long-term environmental accountability in next-generation networks.