The development of data-driven diagnostic models to support medical professionals in decision making has raised interest in the detection and evaluation of various ailments. This paper strives to offer a highly accurate and efficient approach for diagnosing a given ailment with the aid of several machine learning (ML) models viz. Gaussian Naive Bayes (NB), K-nearest neighbour (KNN), CatBoost, XGBoost, support vector machine (SVM), decision tree (DT), random forest (RF), and logistic regression (LR). Four disease datasets, diabetes, Parkinson’s disease, breast cancer, and chronic kidney disease, were extracted from Kaggle and were subjected to these ML approaches. A ten-fold cross-validation procedure was applied to all models using the same datasets to assess the predictive model accuracy. However, the outcomes delivered by the ML models do not surpass the insights offered by existing literature, compelling us to integrate ensemble learning into the prediction system. Consequently, an ensemble learning approach was developed by combining all eight algorithms using a voting classifier to identify the optimal model from the ensemble options. Five evaluation metrics accuracy, precision, recall, F1 score, and Root Mean Square Error (RMSE) were used to verify the models’ performance. Furthermore, Flask—a Python web framework—has been utilized to develop the online interface for the multiple disease prediction system.