Soft computing techniques in fog computing is an emerging research area that focuses on applying methods such as fuzzy logic, genetic algorithms, particle swarm optimization, neural networks, and hybrid intelligent approaches to address uncertainties, imprecisions, and dynamic resource management challenges in fog environments. Research papers in this domain explore applications of soft computing for task scheduling, resource allocation, service placement, load balancing, energy optimization, and fault tolerance in heterogeneous and distributed fog architectures. Studies highlight how these techniques enable adaptive, context-aware, and intelligent decision-making in real-time, considering fluctuating workloads, network variability, and Quality of Service (QoS) requirements. Recent works also investigate hybrid approaches combining multiple soft computing techniques or integrating them with machine learning and optimization algorithms to enhance scalability, resilience, and efficiency in multi-tier fog–edge–cloud systems. Security- and privacy-aware frameworks are increasingly incorporated to ensure reliable and trustworthy operations while managing distributed resources. Applications span smart healthcare, autonomous vehicles, industrial IoT, smart cities, and latency-sensitive multimedia services. Overall, research in soft computing techniques for fog computing enables flexible, intelligent, and efficient management of distributed fog resources, supporting adaptive, high-performance, and resilient next-generation computing systems.