Parkinson's disease (PD) is the second-most common world-wide neuro-degenerative disorder, and its burden is predicted to double by 2040 in parallel with world-wide population ageing. As a consequence of the fact that slight phonatory abnormalities may occur years before clear motor signs, acoustic screening has come to be regarded as an inexpensive and non-invasive pathway to early diagnosis. In this research we compare five popular machine-learning classifiers—Logistic Regression, Multilayer Perceptron, Support Vector Classifier, Random Forest and Gradient Boosting—under one preprocessing and validation pipeline and, importantly, compare several solver or optimiser implementations for each model. Utilizing the 195-sample UCI PD voice dataset, as well as 10 × Repeated Stratified Five-Fold cross-validation, we find that the Multilayer Perceptron model, optimized with the Adam optimiser, has the top mean accuracy of 91.35%, followed closely by Random-Forest–Gini with 91.04%. Friedman and Wilcoxon tests support that these improvements in performance are statistically significant. A follow-up analysis indicates that Adam attains this accuracy premium at a low cost of only 10% energy overhead compared to stochastic gradient descent, highlighting the significance of optimiser selection for both diagnostic accuracy and computational viability. The results show that solver selection is as critical as model family in voice-based PD screener design and encourage future efforts on multi-modal fusion and federated learning to better address present data and privacy limitations.