Flavonols, a subclass of flavonoids, have shown potential anticancer activity across various cancer types. The investigation of their therapeutic effects is supported by multiple cancer and metabolite databases, such as OncoDB, PCIDB, KNApSAcK, ChEMBL, and FooDB, which provide valuable data on biomarkers, plant metabolites, bioactive molecules, and their interactions with cancer-related proteins. In silico approaches, including molecular modeling, ligand-based drug discovery, and structure-based drug discovery, are widely used to identify and characterize flavonol compounds, determine their mechanisms of action (e.g., antitumoral, antiproliferative, antiapoptotic), and predict their interactions with protein targets. These approaches also involve using tools like QSAR models, molecular docking, and molecular dynamics simulations to assess molecular affinity and stability. Additionally, synthetic biology and bioinformatics tools are employed to predict the functions of flavonols and their potential for combined therapies with other drugs. Despite significant advances, challenges remain in predicting the interactions and mechanisms of flavonols due to the complexity of biological systems, variability in individual responses, and incomplete data on flavonol bioavailability and metabolism. To address these limitations, further research utilizing methods like high-throughput screening, metabolomics, and systems biology is needed to deepen the understanding of flavonol’s anticancer effects and their potential for targeted cancer therapies.