Natural Xanthine oxidase (XOD) inhibitors represent promising therapeutic agents for hyperuricemia (HUA) treatment due to their potent efficacy and favorable safety profiles. This study involved the construction of a comprehensive database of 315 XOD inhibitors and development of 28 machine learning-based QSAR models. The ChemoPy light gradient boosting machine model exhibited the best performance (AUC = 0.9371 and MCC = 0.7423). This model identified three potential XOD inhibitors from the FooDB database: daphnetin, 7-hydroxycoumarin, and piceatannol. Molecular docking and dynamics simulations revealed favorable interactions, with piceatannol showing a remarkable stability through hydrogen bonding and hydrophobic interactions. ADME predictions suggested that all three compounds possess desirable drug-like properties and safety characteristics. Subsequent in vitro enzyme inhibition assays validated computational predictions, with piceatannol exhibiting the strongest inhibitory activity (IC