People today suffer from various chronic diseases due to lifestyle choices and environmental conditions. Early prediction of these diseases is crucial to prevent them from worsening. However, it is often challenging for physicians to accurately diagnose these issues independently. A computational model based on big data analytics has numerous applications in the medical field. Advancements in machine learning (ML) and information technology have enabled more accurate recognition of diseases, health emergencies, and conditions. The advent of predictive algorithms has transformed disease diagnosis in the medical field. Previously, patient data records were meticulously examined to predict diseases and develop advanced models for trend analysis. However, with the rise of data analytics-driven intelligence, the evaluation of disease symptoms has become more accurate and efficient. These technologies enable the creation of predictive models capable of analyzing vast amounts of patient data to identify hidden patterns, aiding in early disease detection and diagnosis. This study explores the development of an ensemble ML-based multiple chronic disease prediction system (MCDPS) focused on three major diseases: chronic kidney disease (CKD), liver disease, and heart disease. The system utilizes ensemble ML models, including Decision Tree (DT), Gradient Boosting Classifier (GBC), Extreme Gradient Boosting (XGBoost), and Extra-Trees classifiers (ETC), to predict diseases. Its user-friendly and straightforward design makes it accessible to beginners. The system’s performance in predicting the three chronic diseases has been evaluated and compared with other traditional models. The results show that the DT algorithm achieved an accuracy of 99%, 85%, and 83% for predicting CKD, heart disease, and liver disease, respectively.