Parkinson’s disease is a complex neurodegenerative disorder that involves degeneration of the dopaminergic neurons, commonly affecting the elderly. However, the degeneration process of the neurons is generally slow. Parkinson’s disease symptoms usually appear to develop gradually when approximately half of the nerve cell activity in the substantia nigra has deteriorated. There is no significant test for the diagnosis and detection of Parkinson’s disease till the motor symptom arises. AI has been introduced in the assessment and early diagnosis of the prominent clinical features of Parkinson’s disease. Artificial intelligence is a field that combines computer science and robust datasets. Several ML techniques exist with different algorithms that are established to be effective for diagnosing Parkinson’s disease like K-nearest neighbor, naïve Bayes, support vector machine, logistic regression, artificial neural network (ANN), and decision tree. Neuroimaging techniques are utilized along with the ML techniques to provide more concrete evidence of the disease and thus help in detection at an early stage of Parkinson’s disease. Many such software have been developed using ML and deep learning approaches for the efficient development of artificial intelligence tool that are discussed comparatively in this review. This in turn has aided the medical science and pharmaceutical sciences with large databases, implementation of ML, algorithms, and excellent computing power for the diagnosis and assessment of several diseases.