Recent research in soft-computing techniques applied to cloud computing emphasizes the use of fuzzy logic, neural networks, genetic algorithms, swarm intelligence, and hybrid intelligent systems to address complex problems in cloud infrastructures such as load balancing, resource allocation, scheduling, and security. These approaches leverage the flexibility and adaptivity of soft computing to manage uncertainties and heterogeneous environments typical of cloud data centers and edge/fog extensions. Studies explore combinations of evolutionary computation and learning-based methods to create self-optimizing, autonomous cloud systems capable of responding dynamically to workload fluctuations and QoS requirements. Overall, soft computing techniques are increasingly being integrated into cloud framework designs to achieve enhanced performance, energy efficiency, and robustness in modern cloud-edge ecosystems.