Research on Workload-aware Resource Management in Edge Computing focuses on designing intelligent strategies and frameworks that dynamically allocate and manage computational, storage, and network resources at edge nodes based on the current and predicted workloads. This area addresses challenges such as heterogeneous and resource-constrained edge devices, fluctuating demand, real-time service requirements, and energy efficiency. Key research directions include predictive workload modeling using machine learning, adaptive task scheduling, and dynamic resource scaling to meet latency and Quality of Service (QoS) requirements. Other emerging topics involve workload-aware load balancing, edge–cloud collaborative resource management, energy- and cost-efficient strategies, and context-aware resource orchestration for IoT and mobile applications. Additionally, research on fault-tolerant and privacy-preserving workload-aware frameworks, multi-objective optimization for latency, throughput, and energy, and intelligent edge orchestration represents significant avenues for advancing efficient, reliable, and scalable edge computing systems.