Cloud-native computing has transformed modern applica-tion development, deployment, and management by enabling scalabilityand flexibility. However, the increasing complexity of workloads and dy-namic resource demands challenge traditional scheduling and resourceprovisioning techniques, often leading to inefficiencies. This paper ex-plores AI-driven approaches to optimizing cloud-native scheduling andresource provisioning. By leveraging machine learning, deep reinforce-ment learning, and predictive analytics, AI enhances decision-making,automates scaling, and improves workload distribution. We present acomprehensive review of recent AI techniques applied to container or-chestration, and Kubernetes-based scheduling, analyzing their impacton cost reduction, performance optimization, and resource efficiency.Additionally, we discuss key challenges such as model interpretability,real-time adaptability, and integration with existing cloud and edge in-frastructures. Ultimately, this paper provides insights into the future ofintelligent cloud and edge resource management, emphasizing the neces-sity of AI-augmented strategies to meet the growing demands of next-generation applications.