The present framework uses wireless sensor networks (WSNs) to monitor student health and personalize learning using blockchain technology. By combining IoT sensors, cloud computing, and blockchain technologies, the proposed system creates an intelligent, privacy-preserving environment for collecting, analyzing, and making decisions about health data in real time. Body temperature can also be monitored by wearable and implantable sensors in addition to heart rate and blood pressure. Homomorphic encryption is used to secure the data transmission to the cloud, and smart contracts within hybrid blockchain frameworks are used to validate the data. We use a Bayesian Belief Network classifier to classify events and compute health indices, which provides accurate identification of sensitive and non-sensitive events. Compared to existing methods, the proposed model is more precise, accurate, recallable, and has a higher F1 score, ensuring that student health is monitored efficiently and effectively. By linking health insights with cognitive and behavioral performance indicators, this approach strengthens healthcare data security as well as enhances personalized learning experiences.