Research on Soft Computing Techniques in Fog Computing focuses on applying computational intelligence methods—such as fuzzy logic, genetic algorithms, particle swarm optimization, neural networks, and hybrid approaches—to solve complex, uncertain, and dynamic problems in fog environments. This area aims to enhance decision-making, resource management, task scheduling, and system optimization under imprecise or incomplete information, particularly in resource-constrained and heterogeneous fog networks. Key research directions include fuzzy logic-based resource allocation and task scheduling, evolutionary and swarm intelligence algorithms for optimization of latency, energy, and cost, and hybrid soft computing frameworks combining multiple techniques for improved performance. Other emerging topics involve QoS- and SLA-aware service provisioning, fault-tolerant and adaptive fog management, context- and workload-aware optimization, and integration with edge–cloud collaborative infrastructures. Additionally, research on machine learning-augmented soft computing, predictive analytics, and real-time autonomous decision-making represents promising avenues for advancing intelligent, resilient, and efficient fog computing systems.