This paper investigates the feasibility of utilizing wearable sensor networks within the auspice of Internet-of-Things (IoT) for early health event prediction and enhanced healthcare management’ [2] In total, ten subjects contributed real-time sensor data for BP, temp BP, O2 Saturation and EKG readings. These values are used to train the machine learning models (Apache: Decide Tree DT; Random Forest RF; Support Vector Machines SVM; Artificial Neural Networks ANN) Finally, when the accuracies of models in predicting health response are examined at Table III, it is observed that SVM has become the most accurate model with an accuracy rate of 97.6% and ANN became second with an accuracy rate of %96,44 ( Table II). Moreover, DT and RF also provide a high level of accuracy which were 92.2% and 89.9 % respectively These discoveries, therefore, highlight the importance of machine learning in identifying and managing disorders early thus promoting proactive healthcare by creating individual-specific treatment plans resulting to improved patient outcomes.