Parkinson's Disease (PD) is a nervous system disorder that affects movement and have motor symptoms such as tremors, rigidity, and non-motor symptoms like cognitive impairment and depression. Early diagnosis and prediction of PD are crucial for effective management and intervention strategies. In this work, we work on the utilization of machine learning algorithms that best classifies and predicts the Parkinson's Disease, aiming to provide valuable insights for doctors to take the best decisions regarding patients’ health. The main aim of our project is to predict the Parkinson's disease of a patient using machine learning algorithms in early stage, so that the affected person can be treated without any delay. Comparative study of performances of various machine learning algorithms will be done through calculating various terms like accuracy, precision, F1score, Recall etc. Hence, this work delivers valuable findings by evaluating and contrasting various computational methods and machine learning algorithms employed in the detection and classification of Parkinson's Disease.