Parkinson’s disease (PD) is a progressive neurological disorder that negatively impacts quality of life and involves a range of motor as well as non-motor symptoms. Conventional diagnostic approaches primarily depend on clinical assessments, which are often time-intensive and may lack objectivity. Even with today’s highly technology wise developments and improvements, early illness identification for PD is still very hard. This comparative research looks at the various techniques and algorithms used to assist in the early detection of PD by analyzing various biomedical data, such as voice recording. This study explores the application of several ML models such as K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forest (RF), and Artificial Neural Networks (ANN). The best approach to classified is very difficult, which is applicable for data sets. Initial phase is data preprocessing to clear the noise from dataset, feature selection, later the models of machine learning (ML) employed where the outcome parameters are accuracy score. The proposed work utilizes the ML models to support of detecting the PD which gives the direction to diagnose the patient at early stage. The results demonstrate that ML models can significantly enhance the early diagnosis of PD, offering a reliable and efficient alternative to conventional methods.