Effective resource management in edge-cloud networks is crucial for meeting Quality of Service (QoS) requirements while minimizing operational costs. However, dynamic and fluctuating workloads pose significant challenges for accurate workload prediction and efficient resource allocation, particularly in resource-constrained edge environments. In this paper, we introduce AERO (Adaptive Edge-cloud Resource Orchestration), a novel lightweight forecasting model designed to address these challenges. AERO features an adaptive period detection mechanism that dynamically identifies dominant periodicities in multivariate workload data, allowing it to adjust to varying patterns and abrupt changes. With fewer than 1,000 parameters, AERO is highly suitable for deployment on edge devices with limited computational capacity. We formalize our approach through a comprehensive system model and extend an existing simulation framework with predictor modules to evaluate AERO’s performance in realistic cloud-edge environments. Our extensive evaluations on real-world cloud workload datasets demonstrate that AERO achieves comparable prediction accuracy to complex state-of-the-art models with millions of parameters, while significantly reducing model size and computational overhead. In addition, simulations show that AERO improves orchestration performance, reducing energy consumption and response times compared to existing proactive and reactive approaches. Our live deployment experiments further validate these findings, demonstrating that AERO consistently delivers superior performance. These results highlight AERO as an effective solution for improving resource management and reducing operational costs in dynamic cloud-edge environments.