Early and accurate prediction of Parkinson's disease (PD) can play a crucial role in improving its management and outcome. The aim of this study was to identify the best methodology for PD prediction. A comprehensive review of various prediction methods, including machine learning algorithms such as Support Vector Machines, Random Forest, and Neural Networks, was conducted. The performance of these methods was evaluated using metrics such as accuracy, sensitivity, and specificity.