The rise of the Internet of Things (IoT) has brought about significant changes in the healthcare sector, leading to the development of more advanced healthcare monitoring systems. To ensure that these systems are dependable and effective, it is crucial to conduct performance modeling and analysis. This research is focused on evaluating the performance of healthcare monitoring systems enabled by IoT. It takes into account various factors, including IoT network infrastructure, communication protocols, data processing, and storage. Moreover, the study utilizes machine learning technique that is Artificial Neural Network to assess healthcare data collected through IoT networks. By employing simulationbased methods, this investigation models how the system behaves and evaluates its performance using metrics like response time, throughput, and reliability. The findings from this study provide valuable insights into the performance of these systems, pinpointing areas where improvements can enhance the overall efficiency of healthcare monitoring systems. As a result, this research makes a substantial contribution to the improvement of efficient IoT-enabled healthcare monitoring systems, which, in turn, offer dependable and cost-effective healthcare solutions.