Parkinson’s disease is the neurodegenerative disorder by which more than 10 million people are affected. The dropping of dopamine levels leads to Parkinson’s disease. For diagnosing Parkinson’s disease there are no medical tests are available. The doctors cannot detect the disease through any medical tests like a blood test or scan reports. To prevent this problem in medical sectors, have to predict the problem in a simplified way. The dataset is analyzed by a Supervised machine learning approach to identify various information. The data pre-processing techniques like data cleaning, data validation are done in the collected dataset to avoid noise and missing values. Data visualization is used to visualize the given dataset in different formats like graphs, charts, etc. Symptoms like speech and tremors are used to identify the disease through a machine learning approach. With the best accuracy, Precision, Recall, and Sensitivity, the Graphical User Interface (GUI) is used to show the predicted result.