The rapid growth of Internet of Things (IoT) technology and wearable devices has transformed healthcare by enabling constant monitoring of patient health. However, the large amounts of data generated create issues like delays, high energy use, and strain on network resources, particularly in cloud-based systems. This paper introduces a collaborative fog-cloud setup for healthcare monitoring that improves Quality of service by sending time-sensitive tasks to nearby fog nodes. Therefore, a Genetic Algorithm-Based Task Scheduling for QoS Optimization is proposed. The layered system lowers delays, reduces energy consumption, and better uses bandwidth while relying on cloud resources for complex analysis. A mathematical model for task management and resource optimization is presented, with performance results from simulations. The framework shows great potential for enhancing healthcare systems with real-time, efficient data processing, leading to better patient outcomes.