Currently, the diagnosis of Parkinson’s Disease is being hindered by the lack of proper biomarkers that exist in other diseases. Clinical symptoms often are validated when there is over 60% loss of dopamine in the body. This is simply too late for a treatment process to begin. As a result, the development of an effective detection and monitoring system is a top priority. In this project, a completely novel tool was created using a Convolutional Pose Estimation machine (CPM), that allows for accurate and effective detection and monitoring of Parkinson’s Disease. Using advanced feature extraction algorithms, proper attributes were inputted to a CPM, allowing for a classification/diagnosis to be outputted.  After proper training and evaluation, the CPM was used in a highly advanced Android App that can be used by doctors, patients, and caregivers. The diagnostic model reached an accuracy greater than 95%, higher than any currently released literature. The developed diagnostic tool eliminates the requirement of expensive infrastructure, and instead utilizes the discovered biomarkers to provide a novel, objective approach to PD diagnosis that will aid in the development of pathogenesis-targeted therapeutics, as well as progressive monitoring of Parkinson’s Disease, allowing for effective data collection and analysis over time.