Increasing use of IoT in healthcare industries has led to a significant change across patient tracking and using analytics for diagnosis. However, the accuracy levels of these systems are significantly affected by factors like noisy data, or even distinguishability of signals, and fluctuating nature of IoT environments. Actually, this research proposes an extensive method consisting of innovative pre-processing methods and deep learning models that can overcome these problems. The approach offered cleans up noise, isolates features that are most important for the application, and improves signal quality, all of which would increase signal reliability, especially as it is intended for healthcare use. The system improved maximum signal-to-noise ratio from 10 to 25 dB and has the model accuracy of 92%, and precision and recall higher than the 90%. These outcomes evidence the strength of the proposed approach in the efficient processing of IoT healthcare signals. The implications revealed in the study underscore the application of this framework for increasing the diagnostic accuracy and decreasing the latency and increasing the efficiency of decision-making in real-time patient care monitoring. This work can pave way for other research in the enhancement of IoT healthcare systems with reference to scalability, flexibility, and deployment in various healthcare settings.