The second most prevalent neurological condition, Parkinson’s disease causes considerable impairment, has no known treatment, and lowers a patient’s quality of life. Early detection may aid in preventing or reducing the symptoms. Because we are not progressing at a faster rate. There are numerous data and detection algorithms available for Parkinson’s disease. These are all distinct ethnic traditions. There are numerous databases for Parkinson’s disease currently available, including numerous internet sources and numerous hospital records. Parkinson disease forecasts are quite low because each is a distinct entity. The entire spectrum of medicinal options is not being fully exploited. There are numerous methods for using machine learning and artificial intelligence. Decision Tree Classifier, KNN Neighbors, Random Forest, and Logistic Regression are available for enhance efficacy and prediction. The dataset for Parkinson’s disease is used in the proposed work, and an ensemble model is created by combining all the best predictions made using different classification techniques. The suggested ensemble model with voting classifier beats all current classifiers in terms of high accuracy, precision, and recall for Parkinson disease diagnosis, detection, and prediction. © 2024 Elsevier B.V., All rights reserved.