<p>Internet of Things (IoT) includes a group of connected, versatile devices. In this vast and intricate network of devices, Fog computing (FC) is becoming increasingly important as it helps manage the data flow of the networks. Technology for load balancing (LB) with effective resource allocation (RA) can be utilized to save energy usage and increase overall performance. Consequently, the primary emphasis now is on designing LB approaches to edge and fog scenarios. This research proposes a new model for RA and LB in fog computing (FC). Initially, RA in FC is carried out after getting the tasks from IoT devices. The tasks will be optimally allocated in each layer of FC based on the services. The resources are optimally allocated using a novel KBI-STO algorithm, considering constraints such as makespan, execution time, resource utilization, and MIPS. After the optimal allocation of resources, load balancing is performed optimally via the Kookaburra Integrated Siberian Tiger Optimization (KBI-STO) algorithm under specified migration constraints like migration cost and migration efficiency. Moreover, the proposed KBI-STO algorithm is a combination of the Kookaburra optimization algorithm and the Siberian Tiger Optimization algorithm, in which the innovation lies in the position updation. These enhancements efficiently explore novel areas and prevent early convergence. Finally, the experimental outcomes demonstrate the superiority of the KBI-STO algorithm over existing algorithms for optimal RA and LB in FE, in terms of execution time, makespan, etc., by varying the tasks. Furthermore, the suggested KBI-STO algorithm achieves minimal execution time, below 110&#xa0;s, for varied tasks and varied virtual machines when compared to existing methods like STO, KOA, PFO, JSO, TSO, MPSO, and ACO algorithms.</p>