Parkinson’s Disease (PD) diagnosis often relies on late-stage clinical symptoms, limiting early intervention. This study leverages multimodal machine learning to integrate speech and gait biomarkers for improved early detection. We preprocess speech data from UCI’s Parkinson’s dataset and motor movement data from Synapse.org, applying feature engineering, classification models, and feature selection techniques. Our approach utilizes ensemble learning and feature fusion, enhancing predictive performance and demonstrating the potential of multimodal analysis for non-invasive PD diagnosis.