Participant dropout from interventional studies targeting healthy lifestyles can significantly undermine the validity of study outcomes. Accurate dropout prediction can help mitigate this issue by enabling proactive participant engagement strategies. This study aims to develop a robust Machine Learning (ML) model to predict dropout from a school and community-based interventional study to promote a healthy lifestyle and prevent type 2 diabetes: The Feel4Diabetes study. Using data from 3274 participants across 790 variables, we aim to identify key dropout determinants and enhance ML predictive accuracy. We evaluated three individual machine learning models—Random Forest, XGBoost, and Support Vector Machine (SVM)—based on performance metrics including accuracy, precision, recall, and F1-score. Among these, the Random Forest model emerged as the most effective, achieving an accuracy of 0.80 on the test set, with balanced precision and recall scores. Our study highlights the effectiveness of machine learning methods in predicting dropout in interventional studies promoting healthy lifestyles and preventing type 2 diabetes. Future research will concentrate on refining these models further and exploring additional data sources to enhance their generalizability.