In this thesis, I leverage the wealth of blood transcriptomic, CSF proteomics, and clinical data, including UPDRS and UPSIT scores, meticulously re ning the data quality through thorough preprocessing. Employing a progressive feature selection technique, I pinpoint the most crucial genes, and proteins associated with Parkinson's disease. Subsequently, I deploy a boosting algorithm to construct a diagnostic framework centered around these identified genes and proteins. Additionally, I conduct an in-depth analysis of UPDRS and UPSIT datasets from PPMI, providing a comprehensive comparison. This holistic approach facilitates a more robust understanding of Parkinson's disease, o ering insights for enhanced diagnostic and treatment strategies.