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In the Internet of Medical Things (IoMT)-based real-time telemedicine systems, patients can utilize a wide range of medical devices and sensors, which leads to the continuous generation of massive amounts of data. The high speed of data generation poses challenges in collecting, organizing, processing, and making decisions about patients' emergency levels. Existing methods for classifying (triaging) patients in such environments often yield inaccurate triage levels, necessitating a computational approach to enhance accuracy. This research aims to handle data from multiple heterogeneous sources in IoMT-based real-time telemedicine systems, analyze the data to accurately triage patients with the most urgent cases, and provide swift healthcare services. The proposed solution, the Data Processing with Triaging Model (DPTM), employs a hybrid approach that combines principal component analysis and decision tree algorithms. The model was tested using 55,680 patients with two chronic diseases: heart disease and hypertension. The computational results demonstrate the promising effectiveness of DPTM in accommodating and managing the requests of 55,680 patients. The proposed system achieves an impressive accuracy of 93%, surpassing four other algorithms. In conclusion, DPTM enhances medical services, reduces hospital overcrowding, and ensures accurate services for all patients, with a 93% accuracy rate. [ABSTRACT FROM AUTHOR]