Exploring AI-Based Self-Configuring Slice Allocation Across Hybrid Access Networks

As next-generation networks evolve to integrate terrestrial, wireless, and edge technologies, resource management becomes increasingly complex. This paper presents an AI-powered orchestration system that autonomously configures and manages network slices across hybrid access environments. Leveraging machine learning and predictive analytics, the system dynamically optimizes latency, throughput, and service-level performance in real time, achieving up to 35 % reduction in average latency and a 28 % improvement in throughput across test scenarios. At its core is a smart feedback loop that fuses multiple AI models to continuously adapt to changing network conditions. Experimental evaluations demonstrate that the system not only simplifies configuration and reduces operational overhead by $\mathbf{4 0 \%}$ but also ensures consistent service quality across diverse deployment contexts. This work contributes a scalable, selfmanaging framework that advances the state of intelligent, responsive service delivery in future communication systems.