Abstract
Parkinson's disease (PD) is a progressive neurodegenerative disorder impacting millions globally, with current diagnostic methods often detecting the disease only after significant neuronal damage. This paper explores the transformative role of machine learning (ML) in addressing the pressing challenges of early diagnosis, progression prediction, and personalized treatment of PD. By leveraging multimodal data—ranging from neuroimaging and genetic markers to digital biomarkers captured through wearables—ML algorithms uncover subtle disease signatures and provide unprecedented accuracy in identifying PD at prodromal stages. Key advancements discussed include convolutional neural networks for neuroimaging analysis, random forests for digital biomarker interpretation, and long short-term memory networks for forecasting disease trajectories. Additionally, the integration of federated learning and explainable AI techniques offers promising avenues for enhancing privacy, transparency, and trust in clinical applications. Despite challenges such as model interpretability and biases in training datasets, this paper underscores the potential of ML to revolutionize PD care, enabling data-driven, patient-specific interventions that significantly improve quality of life and disease management.