Parkinson disease (PD) is a progressive neurological disorder that affects movement due to a loss of dopamine-producing nerve cells in the brain. The main motor symptoms are called “parkinsonism” or “parkinsonian syndrome.” Symptoms start slowly (such as tremors, bradykinesia, rigid muscles, balance problems, and behavioral changes). PD detection aims to improve diagnostic accuracy and objectivity to detect the disease early. We use machine learning to detect early onset of PD. Early detection will avoid brain damage and death. Patients, doctors, and the public will greatly benefit from this. This chapter constructed a machine learning model to predict PD: Data is gathered from the online community platform Kaggle; preprocessing is carried out to transform the generated row data into meaningful data; a classification algorithm is used to train the model (in this case, an SVM classifier); and finally, the model is tested on the test dataset to determine its accuracy. Upgrading the healthcare system and promoting awareness of Parkinson disease can lead to early detection and treatment, improving patient quality of life and lowering healthcare expenses. It can reduce stigma and increase social support for patients and their families while improving access to care for underserved communities.