This case study aims to predict Parkinson’s disease using curated data from the Micheal J. Fox Foundation. The goal is to assist physicians by creating a reliable and reproducible tool through machine learning models. Unique to this paper, commonly used subjective predictors like the UDPRS are removed in favor of non-biased objective metrics. Data consisted of demographic, clinical, biological, and genetic subgroups. Validation through training and test splits reveal Random Forest as the best classifier of the disease. A model accuracy of 85% beats current estimations of physician diagnosis. Model predictors are relatively accessible and support the function of a rudimentary initial disease screening. Caudate region-related variables from DaTscan data heavily influence model prediction while BMP derivatives offer further research opportunities. Steps forward involve medical integration and advisement from diagnosing physicians. Alternative designs consider time-series modeling.