Parkinson’s disease (PD) is a progressive neurological disorder that poses significant challenges in early diagnosis and management due to its complex, multi-faceted nature. This article presents an innovative approach to predict the progression of PD using predictive analytics. Our methodology leverages a novel artificial intelligence (AI) model that synthesizes various data inputs, including clinical assessments, genetic markers, and patient-derived sensor data. The proposed model employs a layered architecture that integrates advanced machine learning techniques to capture the dynamic and heterogeneous aspects of the disease progression. By analyzing temporal and cross-sectional data, the model provides valuable predictions that can assist clinicians in crafting personalized treatment plans. The effectiveness of this approach is demonstrated through a series of validations, showing promising results in improving the accuracy and reliability of PD progression forecasts. This work not only contributes to the ongoing efforts in enhancing PD management but also opens new avenues for applying AI in complex disease analytics.