Parkinson’s disease is a developmental neurological problem that affects a large number of population around the world. Early Parkinson’s disease identification is crucial for optimal therapy and control of the condition. In this proposed work, we proposed some machine learning-based techniques for Parkinson’s disease diagnosis using clinical data from the machine learning repository of UCI. We applied machine learning models on this dataset (includes Parkinson’s experimental and control). In the identification of Parkinson’s disease, our methods showed great accuracy, and other relevant factors were also found to be good. We have also compared the various machine learning algorithms to possibly pick the best out of them which may again vary on a different kind of dataset. We also did feature selection and analysis to determine the most essential characteristics for Parkinson’s disease identification. However, we admit that the dataset has several limitations and biases, such as its limited size and reliance on patients from a single institution. We have also attempted to balance the dataset using SMOTE. Nonetheless, our findings imply that Parkinson’s disease identification using machine learning methods might be valuable tools for improving patient outcomes and quality of life. \(\dots \)