Nowadays, enormous of high-speed data streams are produced by devices because of Internet of Things (IoT) applications. Most of these data and requests are handled and managed by cloud computing systems. The data transmission latency associated with moving data from edge devices to the cloud, however, can be intolerable for some applications. In fact, the public network becomes a data transfer bottleneck when a large number of devices are linked to the internet. Here, the advent of fog computing has completely changed the way that standard cloud computing operates by putting processing power closer to the users. By extending cloud computing to the network’s edge, fog computing makes location-aware, low-latency computing services possible. However, because of the dynamic nature of resources, fluctuating network conditions, and wide range of application needs, effective task scheduling in fog- cloud systems continues to be a substantial difficulty. In order to overcome this difficulty, this system is offered, which suggests a Particle Swarm Optimization (PSO) algorithm-based efficient task scheduling framework with a range of offloading techniques. This system looks into cost-aware offloading and priority- based offloading mechanisms for task execution while taking into account various parameters including cost, energy usage, and execution time.