Parkinson's disease is a progressive neurodegenerative disorder affecting movement; hence, early detection would be necessary for effective management. This research explores ML algorithms for PD detection based on voice recordings and clinical measures. The dataset comprises features like vocal fundamental frequency, variation measures, and demographic information. EDA provided insights into the feature distributions and relationships. The algorithms used included LR, KNN, Gaussian Naive Bayes, and support vector classifier, for which appraisal metrics like accuracy, precision, recall, and AUC-ROC were calculated. Ensemble learning through stacking was performed on top of the predictions made by logistic regression, KNN, and SVC. It showed better performance compared to the individual classifiers. The stacked classifier achieved a respectable accuracy of 95%, thereby indicating its success in PD detection. Overall, the above cram shows the potential and effectiveness of ML-based approaches for the early detection of PD, thereby enabling personalized management strategies.