Abstract
While cloud-edge infrastructures demand flexible and sophisticated resource management, 6G networks necessitate very low latency, great dependability, and broad connection. Cloud computing's scalability and agility enable it to prioritize service delivery at various levels of detail while serving billions of users. However, due to resource inefficiencies, virtual machine (VM) issues, response delays, and deadline violations, real-time task scheduling is challenging in these settings. This study develops an AI-powered task scheduling system based on the newly published Unfair Semi-Greedy (USG) algorithm, Earliest Deadline First (EDF), and Enhanced Deadline Zero-Laxity (EDZL) algorithm. The system chooses the best scheduler based on load and work criticality by combining reinforcement learning adaptive logic with a dynamic resource table. Over 10,000 soft real-time task sets were utilized to evaluate the framework across various cloud-edge scenarios. When compared to solo EDF and EDZL solutions, the recommended hybrid method reduced average response times by up to 26.3% and deadline exceptions by 41.7%. The USG component achieved 98.6% task stimulability under saturated edge settings, indicating significant changes in workload. These findings suggest that the method might be useful for applications that need a speedy turnaround. This architecture is especially well-suited for autonomous systems, remote healthcare, and immersive media, all of which require low latency and dependability, and it may be extended to AI-native 6G networks.