The exponential growth of produced data by healthcare monitoring devices poses a substantial challenge for conventional fog-based computing frameworks. Fog computing, a dispersed computing prototype that expands fog computing capabilities to the network's edge, emerges as a promising solution to address this challenge. This paper, proposes a technique for offloading computations for healthcare monitoring in fog computing, aiming to minimize task completion time, consumption of energy, execution time ratio and response time analysis. Enhancing Healthcare Monitoring with Optimal Computation Offloading in Fog Environment specifies that the research is focused on improving healthcare monitoring systems through the use of fog computing. In this approach, data processing is carried out closer to the source, such as medical devices or sensors, instead of depending only on centralized cloud servers. The "computation offloading" technique is moving computational workloads from less powerful devices to edge or fog nodes with more processing power. By using this method, the research seeks to improve real-time data processing, minimize latency, maximize resource use, and improve security in healthcare monitoring by retaining confidential data closer to its source. The goal of the study is to show how this strategy might result in healthcare monitoring systems that are more effective and efficient, especially when quick decisions and great data security are required. The proposed technique dynamically offloads computation tasks to fog nodes based on real-time network conditions, resource availability, and task characteristics. It emphasizes the achievement of superior performance metrics including the shortest job completion time, lowest energy consumption, and minimal cost compared to existing task offloading methods within healthcare contexts. The technique notably achieves a reduction of up to 31.1% in task completion time, 66.67% in energy consumption, and 20% in execution time ratio compared to existing task offloading methods in healthcare contexts. Additionally, it improves response time by 40%, demonstrating superior performance metrics. It conducts a thorough assessment of the proposed technique’s effectiveness through key performance indicators such as Task Completion Time, Energy Consumption, Execution Time Ratio, and Response Time Analysis. Finally, a detailed comparative analysis against established techniques enriches the discussion, providing valuable insights into the superiority of the proposed technique.