The Internet of Things (IoT) has become an essential part of numerous domains, including smart homes, agriculture, healthcare, education, and industrial automation. As IoT usage grows, the number of connected devices rapidly increases, causing substantial network congestion due to the overwhelming volume of data requests. While providing significant computational resources to IoT devices may relieve this issue, these devices are intrinsically limited by considerations such as battery life, processing power, and storage capacity. To overcome these limitations, IoT devices frequently rely on cloud servers for work offloading, resulting in improved resource management. However, cloud-based offloading causes significant latency, reducing reaction time. Fog computing has emerged as a computational layer between IoT devices and the cloud, seeking to minimize latency and improve real-time processing. Offloading tasks to fog nodes can drastically reduce reaction times in a cloud-fog environment. Nonetheless, efficient resource management in fog computing remains a significant difficulty, demanding the creation of an ideal task-offloading strategy. This paper proposes a Multi-Objective Task Offloading using the Sparrow Search Algorithm (MOTO-SSA) method for optimizing job allocation from IoT devices to fog nodes. The suggested method approaches task offloading as a multi-objective optimization problem, to decrease response time and execution cost. To assess MOTO-SSA’s usefulness, its performance is compared to those of existing algorithms such as Ant Colony Optimization, Particle Swarm Optimization, Artificial Bee Colony, and Round Robin. Simulation results show that MOTO-SSA achieves greater convergence and beats previous strategies in terms of response time and execution costs, making it a promising solution for effective IoT task management in fog computing scenarios.