Parkinson Disease (PD) is brain disorder that affects the central nervous system that results in damage of nerve cells causing dopamine to drop. PD has a severe effect on vocal features termed as Dysarthria symptoms including varied pitch, extended pauses, monotonous and speaking slowly or with a slur. In this paper, a dataset containing various vocal features are taken as input to analyze the performance of various Machine Learning algorithms including Naive Bayes, Random Forest Classifier, Support Vector Machines (SVM), Linear Regression, K Nearest Neighbor (KNN) and Neural Networks such as ANN and LSTM. The best classification accuracy was obtained by ANN around 90.00%.