1Shaik Mubhashira, 2Rasipogula Swapna, 3Shaik Sadhik, 4T V Sai Charan,5Gunja Narendra, 6Mr.V.V.B. Chari 03 04 Hypertension is one of the most common chronic health conditions and a major risk factor for cardiovascular diseases worldwide Early detection and continuous monitoring are essential to prevent severe complications such as stroke heart attack and kidney failure Traditional diagnosis relies on periodic blood pressure measurements and clinical assessment which may miss early warning signs This project proposes a machine learningbased healthcare system for the prediction of hypertension using patient health data The system analyzes clinical parameters such as age body mass index BMI blood pressure readings cholesterol levels lifestyle factors and family history Data preprocessing ensures quality and consistency of medical records Multiple machine learning algorithms are trained to classify individuals as hypertensive or nonhypertensive Feature selection improves model accuracy and efficiency The system provides early risk prediction and decision support for healthcare professionals Performance is evaluated using accuracy precision recall and F1score Automated alerts notify users of potential hypertension risks The model supports preventive healthcare and personalized treatment planning Secure data handling ensures patient privacy The proposed approach enhances accessibility accuracy and efficiency in hypertension management Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026