Parkinson’s Disease (PD) is a progressive neurodegenerative disorder characterized by motor and non-motor symptoms, including subtle changes in speech. Traditionally, PD diagnosis relies on motor symptom observation or imaging techniques, which often occur after significant neuronal damage and can be expensive or inaccessible. Voice alterations, such as reduced pitch variation and vocal tremor, tend to appear earlier and are measurable through acoustic features. This study investigates the potential of using machine learning models trained on voice signal features to classify PD and distinguish it from healthy controls, with the goal of developing a non-invasive, cost-effective screening tool. Methodology The authors used a publicly available dataset from the UCI Machine Learning Repository consisting of voice recordings from 31 individuals (23 with PD and 8 healthy controls). Each participant produced a 36-second sustained vowel, from which 24 acoustic features were extracted. The data was cleaned and normalized using StandardScaler. Initially, feature selection via SelectKBest was considered, but all features were ultimately retained due to minimal redundancy. The dataset was split 70/30 for training and testing. To address class imbalance, SMOTE (Synthetic Minority Oversampling Technique) was applied to the training set. Five machine learning models—K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Multi-layer Perceptron (MLP)—were trained and optimized using GridSearchCV. Results Without SMOTE, models showed moderate accuracy and poor balance in precision/recall, especially for the minority healthy class. After applying SMOTE, all models demonstrated improved performance. KNN and SVM achieved accuracies of 88% and 95%, respectively, while MLP performed best with 98.3% accuracy and 100% precision. The MLP used a simple architecture: one hidden layer with 16 neurons, ReLU activation, and the LBFGS optimizer. These results validated the hypothesis that class balancing and hyperparameter tuning significantly enhance classification accuracy. Conclusion The study concludes that voice signal features can be effectively used for PD classification through machine learning. By addressing dataset imbalance and optimizing model parameters, the authors achieved state-of-the-art performance with a simple neural network. The findings emphasize the feasibility of non-invasive, voice-based diagnostic tools for early PD detection. Impact of Research This research offers a promising pathway toward accessible and scalable PD screening solutions. Its implications extend to remote and underserved regions where traditional diagnostics are limited. The use of open datasets and interpretable models enhances reproducibility and future validation. Looking forward, integrating voice analysis with other modalities—such as handwriting, gait, and facial movement—could lead to robust multimodal diagnostic platforms powered by AI. This work represents an important step toward digital biomarkers in neurology.