<p>Parkinson’s disease (PD) is one of the most prevalent neurodegenerative disorders, with a sharp increase predicted. Classifying and predicting PD at an early stage is crucial. Application of artificial intelligence (AI) is a significant factor in the diagnosis of various disorders. Based on patient data, machine learning (ML) and deep learning (DL) can automatically predict PD. This research aims to develop an automated approach for early PD prediction based on vocal symptoms and AI techniques. To forecast PD, specific AI models have been implemented. Extreme gradient boosting (XGB or XGBoost), artificial neural networks (ANN), Naive Bayes (NB), K-nearest neighbor (KNN), multilayer perceptron <b>(</b>MLP), logistic regression (LR), support vector machine (SVM), and ridge classifier with cross-validation (RidgeCV) were among the AI models used. The dataset was subjected to various data preprocessing approaches, such as Min–Max scaling and synthetic minority over-sampling technique (SMOTE). Sensitivity, accuracy, F1-score, precision, specificity, and the area under the receiver operating characteristic (ROC) curve (AUC) were among the evaluation measures used to assess the effectiveness of the implemented AI system. The results demonstrated that, with 98% accuracy, 97% precision, 100% sensitivity, 98% F1-score, 97% specificity, and 100% AUC, the XGB model utilizing SMOTE approach achieved the best results. With the proposed approach, patients can forecast their PD early. The proposed work contributes significantly to the field of neurodegenerative disease research by demonstrating the effectiveness of AI techniques in early PD prediction, which can have profound implications for patient care and treatment strategies.</p>