Recent research in multi-objective optimization in cloud computing focuses on developing advanced algorithms that balance conflicting goals such as cost, energy consumption, resource utilization, response time, and service reliability. Techniques like Genetic Algorithms, Particle Swarm Optimization, Cuckoo Search, and Deep Reinforcement Learning are widely adopted to achieve efficient trade-offs in dynamic and heterogeneous cloud environments. These studies emphasize optimizing multiple objectives simultaneously rather than relying on a single metric, leading to more adaptive and intelligent decision-making for resource scheduling, load balancing, and service provisioning. The integration of hybrid metaheuristics and machine learning-based multi-objective models has further enhanced the ability to meet Quality of Service requirements while maintaining energy and cost efficiency across distributed cloud infrastructures.