A highly sophisticated swarm intelligence-oriented algorithm in task offloading and scheduling specifically designed for the fog computing environment advances the efficiency of task execution along with a reduction in energy consumption and better load balancing on the fog nodes. Traditional offloading techniques such as round-robin often have resource allocation problems that lead to constraints in performance and an increase in energy consumption. The algorithms use PSO for offloading and ACO for scheduling the tasks and thereby can be used in dynamic as well as real-time decision-making to allocate tasks based on nodes’ capabilities and workloads. Extensive simulations have been performed to compare the proposed algorithm with traditional fog computing methodology on key metrics such as execution time, energy consumption, load imbalance, and task completion rate, as well as a look at resource usage. The results indicate a substantial improvement in all performance parameters and thus exemplify good design through swarm intelligence into optimal fog computing configurations. These results form the basis for designing scalable, energy-aware, and fair task scheduling techniques and algorithms for heterogeneous infrastructures of fog computing.