This document presents research on using machine learning algorithms to detect Parkinson's disease using voice and speech parameters. The researchers used Support Vector Machine (SVM) and XGBoost algorithms to build models for Parkinson's disease detection and achieved 94.87% accuracy. Voice and speech data from patients was analyzed to identify important features for the models. SVM and XGBoost were able to accurately classify patients as having Parkinson's disease or not based on their voice and speech features, demonstrating the potential of artificial intelligence and machine learning techniques for Parkinson's disease diagnosis.