The loss of dopamine-producing neurons is a defining feature of Parkinson’s disease (PD), a prevalent neurological disorder. Initial identification is challenging since symptoms come gradually. Intense learning and artificial intelligence techniques have shown considerable promise in recent years for improving the projected accuracy of the disease’s diagnosis using a variety of speech and physiological data sets. Deep learning and conventional machine learning approaches used for disease prediction are studied. It examines the advantages of deep learning models for achieving cutting-edge results and presents key studies, contrasting their techniques and outcomes. Finally, the accuracy and limitations of each methodology are discussed.