Health management centres play a crucial role in managing and maintaining comprehensive health information systems, which include patient records, medical histories, and other vital data. Recent advancements in technology, including wireless communication networks, smart applications, and sensors, have significantly improved the quality of life by enabling more efficient health monitoring and management. Central to these advancements is the Internet of Things (IoT), which facilitates the processing, distribution, and access to essential data, thereby enhancing healthcare delivery. The purpose of this research is to develop a novel data processing system on an IoT platform specifically designed for real-time health monitoring of athletes within sports medicine and health management centres. The proposed system leverages public healthcare datasets stored in the cloud, which are pre-processed using Min-max normalization. Key features are extracted using Independent Component Analysis (ICA) to ensure accurate and relevant data processing. This study introduces a hybrid Magnetic Optimized Dynamic Support Vector Machine (HMO-DSVM) method, which is designed to optimize health monitoring for athletes by improving the accuracy and reliability of health assessments. The performance of the proposed system is evaluated against standard models, with experimentation conducted to validate the enhanced performance of the HMO-DSVM approach. Key performance metrics such as accuracy, precision, specificity, F1-score, and sensitivity are used to assess the effectiveness of the system. As intelligent technologies continue to evolve, their integration into sports medicine and athlete health management will be pivotal in shaping the future of healthcare delivery and monitoring, ultimately leading to improved health outcomes and enhanced athletic performance.