Data has become a crucial component of every industry in this digitised age, including healthcare. By using data from all healthcare sources, including patient demographics, prescriptions, vital signs, doctor observations, laboratory data, billing data, data from various wearable sensors, etc., healthcare business generates enormous amounts of digital data. Data has become a crucial component of every industry in this digitised age, including healthcare. This study, by using machine learning to the field of data analytics, suggests a new approach to tracking athlete fitness and performance. The player's on-field performance and health are tracked by a wearable optical sensor, with data being relayed through an Internet of Things module. The player's vitals have been assessed from the collected data using a naive Bayesian convolutional vector network (NBCVN). Prediction accuracy, precision, MSE, AUC, and F-1 score are measured and analysed experimentally on the dataset gathered from athletes' and players' sensor monitoring devices. On three openly accessible standard datasets, the system has been compared against the traditional methodologies. The experimental findings demonstrate that suggested strategy outperforms current state-of-the-art approaches. Proposed method achieved 95% prediction accuracy, 88% precision, 51% MSE, 81% AUC, and an 85% F-1 score. [ABSTRACT FROM AUTHOR]