The complexity of logistics combinatorial optimization problems including vehicle routing, warehouse scheduling, and dynamic delivery has increased because of rising demand and evolving constraints. Metaheuristics show effectiveness but need problem-specific tuning and demonstrate limited general applicability. This research presents a learning-based hyper-heuristic framework which operates at high abstraction levels to select or generate low-level heuristics through dynamic decision-making based on problem features and real-time performance feedback. The proposed system uses reinforcement learning to select heuristics while pursuing adaptability, scalability and domain independence. Additionally, the framework demonstrates its effectiveness through benchmark dataset experiments, which show better solution quality and improved computational efficiency and robustness compared to traditional metaheuristics. Moreover, the framework shows its capability to perform automated decision-making while minimizing human involvement and demonstrating effective adaptation to changing logistics environments. Finally, this research presents an adaptable intelligent optimization system which enhances operational efficiency and resilience in smart supply chain systems.