This project analyzes voice recordings to identify early biomarkers of Parkinson’s Disease using deep learning. It involves extracting vocal features, modeling speech irregularities, and training neural networks for classification. The approach enables non-invasive, cost-effective early screening by detecting subtle changes in speech patterns associated with PD onset.