Parkinson’s Disease presents a significant worldwide health challenge, impacting millions with its motor and non-motor symptoms. Timely detection and precise prediction are crucial for improving patient outcomes. In this project, we introduce an innovative strategy for Parkinson’s Disease risk prediction utilizing a Genetic Algorithm (GA) in conjunction with machine learning techniques. The GA optimizes the selection of genetic markers and pertinent features from an extensive dataset comprising demographic details, medical records, and clinical assessments linked to Parkinson’s Disease. Through iterative refinement, the GA identifies the most informative feature subset vital for predicting disease susceptibility. Different machine learning models are trained using the chosen features. The efficacy of each model is assessed and our proposed methodology demonstrates superior accuracy in predicting Parkinson’s Disease risk compared to existing approaches. By combining GA-based feature selection with machine learning models, our approach enables precise and effective Parkinson’s Disease prediction, facilitating early diagnosis and tailored therapeutic strategies. This study underscores the potential of genetic algorithms in enhancing predictive models for neurodegenerative conditions like Parkinson’s Disease.