Recent research in Meta-Heuristic based Energy Optimization in Cloud Computing explores advanced algorithms to minimize energy consumption and improve overall efficiency in cloud data centers. These studies employ meta-heuristic techniques such as Genetic Algorithms, Particle Swarm Optimization, Ant Colony Optimization, Whale Optimization, and hybrid or quantum-inspired models to balance workload distribution, optimize task scheduling, and enhance resource utilization. By integrating predictive modeling, reinforcement learning, and opposition-based learning strategies, researchers achieve adaptive and dynamic energy management under varying workloads. The focus is on reducing both computing and cooling energy while maintaining Quality of Service (QoS) and minimizing operational costs. Overall, meta-heuristic-driven energy optimization frameworks enable sustainable and scalable cloud environments through intelligent, self-adaptive, and power-aware decision-making mechanisms.