Cloud-native computing has transformed modern application development, deployment, and management by enabling scalability and flexibility. However, the increasing complexity of workloads and dynamic resource demands challenge traditional scheduling and resource provisioning techniques, often leading to inefficiencies. This paper explores AI-driven approaches to optimizing cloud-native scheduling and resource provisioning. By leveraging machine learning, deep reinforcement learning, and predictive analytics, AI enhances decision-making, automates scaling, and improves workload distribution. We present a comprehensive review of recent AI techniques applied to container orchestration, and Kubernetes-based scheduling, analyzing their impact on 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 infrastructures. Ultimately, this paper provides insights into the future of intelligent cloud and edge resource management, emphasizing the necessity of AI-augmented strategies to meet the growing demands of next-generation applications.