Recent research in meta-heuristic-based profit maximization in cloud computing explores advanced optimization methods to enhance provider profitability while maintaining high-quality service delivery and efficient resource allocation. Techniques such as Genetic Algorithms, Particle Swarm Optimization, Ant Colony Optimization, and Hybrid Metaheuristics are widely used to dynamically allocate resources, minimize operational costs, and maximize revenue through optimal pricing and workload distribution. These approaches effectively handle the complex, multi-dimensional nature of cloud environments by balancing trade-offs between cost, energy, and performance. Overall, meta-heuristic-driven profit maximization frameworks provide adaptive and scalable solutions that improve decision-making and profitability for cloud service providers under varying user demands and infrastructure constraints.