AI models play a crucial role in advancing technologies for the effective and early detection of Parkinson’s disease using the patients’ biomarkers. Parkinson’s disorder is neurogenerative that results in disruptive motor functions. The biomarkers used by AI models include symptoms for motor functions and non-motor functions also. This study proposes a comparative analysis of machine learning model for Parkinson’s detection. Parkinson’s EEG dataset is used to build the model that comprises vocal biomarkers and other medical biomarkers allowing non-invasive disease detection, and the evaluation is done over accuracy, precision and recall of the model. Another important metric is the area under the curve. The study contributes a comparison of support vector machine with three boosting ensembles (AdaBoost, GBoost, XGBoost). The results demonstrate that XGBoost outperforms other candidate models with accuracy of 94.9% and AUC of 98.0%. It concludes that boosting ensembles are competent to detect Parkinson’s disease over voice signals effectively. Further, this study lays a directive guidance to future researchers to adopt the suitable model for their problem statement.