This chapter focuses on proactive autoscaling strategies for cloud-native applications through predictive resource allocation. It begins by examining the challenges of cloud-native architectures and the limitations of reactive autoscaling in responding to dynamic workloads. Proactive autoscaling is presented as a solution, leveraging predictive models to anticipate demand and optimize resource allocation. The InformerAutoScale framework is introduced, including its architectural overview, key algorithms, and core innovations that enable efficient scaling decisions. The methodology section details the data processing pipeline, Kubernetes integration, and execution layer for real-time implementation. Performance evaluation demonstrates the framework’s effectiveness in maintaining low latency, high resource utilization, and cost efficiency. The chapter concludes with a discussion of insights, highlighting the practical advantages of predictive autoscaling for both cloud and edge environments. By integrating predictive analytics with Kubernetes orchestration, the approach ensures reliable and intelligent resource management in dynamic application scenarios.