Parkinson’s disease is a progressive brain disorder that lowers dopamine levels and leads to both movement and non-movement difficulties. Because its early signs are subtle and the course of the disease can vary widely between patients, diagnosis and monitoring remain challenging. In this study, we present a machine learning framework designed to improve risk assessment by using multimodal data from the Parkinson’s Progression Markers Initiative (PPMI). At the core of our approach is a sequential and progressive model that integrates motor and non-motor clinical features with information about how symptoms change over time, enabling more reliable prediction of both disease risk and trajectory across diverse patient groups. The framework’s performance is evaluated with standard measures of diagnostic accuracy, sensitivity, and progression forecasting. A review of existing work shows that fewer than 15% of machine learning studies on Parkinson’s use multimodal data in depth, and fewer than 5% combine sequential symptom tracking with progression risk scoring. This highlights the novelty of our approach. Our findings shed light on which features are most important, demonstrate the potential of longitudinal tracking for emerging symptoms, and suggest how such models can support earlier detection and more personalized care. We also discuss practical factors for applying clinical data in scalable and reliable healthcare AI systems. Overall, this work provides insights that can guide future research and inform best practices in using machine learning for Parkinson’s disease management.