The timely identification of Parkinson’s disease (PD) holds significant importance due to its lack of a curative medical procedure; instead, it can only be managed through medical intervention during its initial phases. However, pinpointing PD in its early stages poses a considerable challenge, given the absence of a definitive diagnostic test. A neurology specialist relies on the patient’s medical background and symptoms for PD diagnosis. Since this procedure is not systematic and is solely based on human experience, it is prone to human errors. To solve every one of these issues, Parkinson’s Disease Early Diagnosis with Extra-Trees (PDEDX) expert system is proposed in this research for the early diagnosis of Parkinson’s disease. The expert system, as proposed, applies the oversampling SMOTE technique to address the issue of class imbalance, Boruta feature selection to perform feature selection, and the classification is done using bagging ensemble Extra-Trees (ET) to overcome the variance and overfitting problem of the single-classifier-based model. The suggested model, PDEDX, demonstrates a notably superior performance in terms of accuracy and F1-score when compared to a range of single-classifier-based models, ensemble models, and other models documented in the literature.