In this research, we propose real-time health tracking in IoT-based healthcare systems. Continuously gathering, processing, and interpreting biological data from different sensors is the goal. The approach focuses on signal preparation, including noise removal, normalization, and frequency domain translation for further analysis. Advanced methods include statistical thresholding, harmonic analysis, and signal quality monitoring, which may identify health issues like rhythms. The technique offers outstanding accuracy, sensor sensitivity, and minimal battery utilization, improving real-time smart health monitoring systems. A complete research study reveals that the recommended technique outperforms others in reaction time, signal-to-noise ratio, sensor durability, and real-time data processing. This strategy keeps patient data sensitive and secure. High comfort, extended sensor lifespan, and cost-effectiveness make the proposed technique a solid performance-price balance. This makes it suitable for most. It improves IoT-based healthcare systems, provides precise health monitoring, and speeds up medical treatments. When coupled, these technologies enable tailored healthcare advancements that will enhance patient outcomes via consistent, effective, and safe health monitoring.