Kubernetes has become the de facto platform for orchestrating containerized applications, offering robust autoscaling features to manage dynamic workloads. The Horizontal Pod Autoscaler (HPA) adjusts the number of pod replicas based on live performance metrics such as CPU and memory utilization, while the Vertical Pod Autoscaler (VPA) fine-tunes per-pod resource requests and limits. However, using HPA and VPA independently often results in conflicting scaling actions. This paper proposes a hybrid autoscaling framework that coordinates both mechanisms through a custom algorithm featuring cooldown windows and sequential scaling logic. Experimental evaluation demonstrates that the hybrid model improves resource utilization, stabilizes application performance, and reduces overhead under fluctuating workloads. Future directions include extending the framework with Kubernetes Cluster Autoscaler for node-level optimization and incorporating cost-aware, AI-driven strategies. These contributions advance efficiency, scalability, and sustainability in cloud-native deployments, making the work relevant for both researchers and practitioners in cloud performance engineering.