Recent research in Meta-Heuristic based Load Balancing in Cloud Computing emphasizes the integration of advanced optimization techniques to achieve efficient task distribution, improved system performance, and energy-efficient resource utilization. Meta-heuristic algorithms such as Genetic Algorithm, Particle Swarm Optimization, Ant Colony Optimization, Firefly Algorithm, Grey Wolf Optimizer, and Hybrid Meta-Heuristics are widely explored to address the NP-hard nature of load balancing problems in large-scale and heterogeneous cloud infrastructures. These methods dynamically adapt to workload variations, optimize multi-objective parameters like response time, makespan, and cost, and enhance the quality of service. Recent studies also incorporate AI-enhanced and hybridized meta-heuristics with deep learning and fuzzy systems to ensure intelligent, scalable, and self-adaptive load balancing across cloud–edge–fog ecosystems.