<![CDATA[Metabolic syndrome (MetS) is a cluster of interrelated risk factors, including hypertension, dyslipidemia, central obesity, and insulin resistance, significantly increasing the likelihood of cardiovascular diseases and type 2 diabetes. Early identification of hypertension, a key component of MetS, is essential for timely intervention and effective disease management. This research aims to develop a hybrid machine learning model that integrates XGBoost classification with K-Means clustering to enhance or strengthening of hypertension prediction and identify distinct patient subgroups based on metabolic risk factors. The dataset consists of 1,878 patient records with metabolic parameters such as systolic and diastolic blood pressure, fasting glucose, cholesterol levels, and anthropometric measurements. Model performance was assessed using accuracy, precision, recall, F1-score, and ROC-AUC. The proposed XGBoost model achieved an outstanding classification performance with 98% accuracy, 98% precision, 98% recall, 98% F1-score, and an ROC-AUC of 1.00. K-Means clustering further identified five distinct patient subgroups with varying metabolic risk profiles. The findings underscore the potential of machine learning-driven decision support systems in improving hypertension diagnosis and MetS management.]]>
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