Research on Soft Computing Techniques in Cloud Computing focuses on applying computational intelligence approaches—such as fuzzy logic, genetic algorithms, particle swarm optimization, neural networks, and hybrid methods—to solve complex, uncertain, and dynamic problems in cloud environments. This area aims to enhance decision-making, resource management, task scheduling, and system optimization under incomplete or imprecise information. Key research directions include fuzzy logic-based resource allocation, evolutionary algorithm-driven task scheduling, neural network-based workload prediction, and hybrid soft computing frameworks for multi-objective optimization. Other emerging topics involve energy-efficient and cost-aware cloud operations, SLA- and QoS-aware service provisioning, fault-tolerant and adaptive cloud management, and intelligent orchestration in heterogeneous and large-scale cloud infrastructures. Additionally, integrating soft computing with machine learning and reinforcement learning for predictive, autonomous, and real-time cloud optimization, as well as applying these techniques to edge–cloud and federated-cloud scenarios, represents promising avenues for advancing intelligent and resilient cloud computing solutions.