Recent research in meta-heuristic algorithm-based optimization of resource allocation in cloud computing emphasizes developing intelligent strategies to enhance performance, reduce energy consumption, and ensure cost efficiency in dynamic cloud environments. Techniques such as Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Cuckoo Search are widely applied to optimize task-to-resource mapping, load distribution, and service-level agreement compliance. By integrating these algorithms or using hybrid meta-heuristic frameworks, researchers have achieved better trade-offs between computational efficiency and energy usage while minimizing makespan and execution cost. Overall, meta-heuristic-driven resource allocation models demonstrate significant potential for adaptive, scalable, and energy-aware optimization in modern cloud infrastructures.