The AlzyFinder Platform represents a significant advancement in the field of Alzheimer's disease (AD) research, providing a comprehensive and user-friendly tool for ligand-based virtual screening and network pharmacology. Our findings demonstrate that AlzyFinder effectively predicts interactions between candidate molecules and over 85 key proteins implicated in AD, utilizing advanced machine learning models that have been rigorously validated. The platform's ability to identify potential multitarget ligands highlights its innovative approach to addressing the complex and multifactorial nature of AD, which is often inadequately tackled by traditional single-target therapies. The integration of robust validation techniques, including a soft-voting ensemble method, has shown to enhance the accuracy and reliability of predictions, thereby facilitating the identification of compounds that may exert systemic therapeutic effects. The outputs generated by AlzyFinder, including interaction data presented in various formats such as tables, heatmaps, and interactive networks, provide researchers with valuable insights into the potential mechanisms of action of drug candidates.