One of the most common diseases affecting the global public health, Parkinson's disease (PD) is getting worse every day and has already affected several nations. As a result, it is crucial to forecast it at a young age, a task that has proven difficult for experts because disease symptoms typically appear inmiddle-aged or older people. The model in this study is developed utilising a variety of machine learning approaches, including adaptive boosting, bagging, neural networks, support vector machines, decision trees, random forests, and linear regression. It focuses on the speech articulation difficulties symptoms of PD affected persons. Various criteria, including accuracy, the receiver operating characteristic curve (ROC), sensitivity, precision, and specificity, are used to assess how well these classifiers perform.

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