Among the different neurodegenerative diseases, Parkinson's disease ranks after Alzheimer’s disease, which is the first neurodegenerative disease affecting the population worldwide. It is a slowly progressive degenerative disorder of neurons that usually affects the population above 50 years. The disease heterogeneity appears as a major barrier to the available standard treatment approaches. The basic concept behind customizing PD treatment is continuous monitoring, tailored diagnosis, and individualized treatment. The recent advancements made in the area of Artificial intelligence (AI) and machine learning (ML), and their penetration into the field of medical science, have opened the way to reach the goal of precision medicine for PD. The AI/ML framework uses algorithms that integrate data from diverse sources, including imaging, clinical, genetic, biomarker, and wearable sensor data, which assist in early detection, subtype categorization, disease prognosis, and recommendations for customized therapy. The paper investigates the utility of AI/ML in reinforcing precision therapy for Parkinson's disease, with the majority focus on predictive analytics, repurposing of drugs, digital biomarkers, and regimens for customized intervention. It also highlights important applications of advanced treatment approaches, which include the use of deep brain stimulation, optimizing the settings, and forecasting the side effect profile of levodopa and other dopamine agonists, the motor issues, and the monitoring of disease trajectories using digital health technologies. Additionally, it also discusses the points of concern that are linked with the use of AI/ML, such as algorithmic transparency, heterogeneity of data, ethical issues, and the future of automated precision medicine in PD.