Recent research in Workload-aware Energy Management in Cloud Computing focuses on dynamically adapting energy consumption based on real-time workload variations to enhance efficiency and sustainability in cloud data centers. Advanced models use workload prediction techniques combined with machine learning, fuzzy logic, and meta-heuristic optimization to allocate resources intelligently while minimizing energy waste. These studies integrate workload forecasting with energy-efficient scheduling, VM consolidation, and renewable energy utilization to balance performance, cost, and power usage. Workload-aware strategies also emphasize proactive resource scaling and carbon-aware workload distribution across cloud and fog layers. Overall, the latest works aim to achieve sustainable cloud operations by harmonizing workload demands with energy-efficient resource management policies.