Autoscaling is a key capability in cloud-native systems, where dynamic workloads, heterogeneous environments, and latency-sensitive applications require efficient and adaptive resource management. Traditional reactive approaches based on fixed thresholds often respond too late, leading to resource imbalance, performance degradation, and unstable scaling behavior. Recent advances in predictive models, Kubernetes Custom Resource Definitions (CRDs), Monitor-Analyse-Plan-Execute (MAPE) based control loops, and federated learning (FL) have enabled more proactive and autonomous autoscaling strategies. This paper presents a structured review of these developments. It first introduces a taxonomy of autoscaling techniques based on triggers, targets, prediction models, and evaluation metrics. It then examines predictive autoscaling approaches and CRD-based mechanisms, including Kubernetes operators and reconciliation workflows. Further, it analyses autoscaling in federated learning environments, highlighting reactive and proactive strategies alongside privacy-preserving techniques and container-level isolation. The paper also discusses drift-aware and uncertainty-aware autoscaling, incorporating concepts such as the Autoscaling Drift Index (ADI), feedback-driven correction, and stability control for heterogeneous workloads. Finally, it outlines open challenges and future research directions, providing a foundation for next-generation intelligent predictive autoscaling in cloud-edge environments.