Hypertension in children is a growing public health concern, often linked to increasing rates of obesity, poor dietary habits, and sedentary lifestyles. The research explores the prevalence of hypertension among school-going children aged 6–18 years in Bangalore using machine learning techniques to identify hypertensive cases, with a particular focus on body mass index (BMI). Data were collected from 384 children, and machine learning algorithms were applied to analyze the relationship between BMI and blood pressure levels. Results showed that BMI is a strong determinant of hypertension risk, with higher BMI categories showing significantly elevated blood pressure readings. The empirical study underscores the potential of using machine learning models for early hypertension detection and emphasizes the need for targeted interventions for children at risk, based on BMI. The findings suggest that integrating machine learning into routine health screenings could enhance the early identification of hypertension in children, facilitating timely intervention and management.