Flavones, a subclass of flavonoids, have demonstrated promising antioxidant and anticancer properties, making them attractive candidates for drug development. This chapter provides an overview of the importance of in silico methodologies for predicting the bioactive potential of flavones and guiding their experimental evaluation. Computational tools such as molecular docking, molecular dynamics, ADMET prediction, and structural modeling enable the efficient screening and characterization of flavone interactions with biological targets involved in cancer. Additionally, relevant bioinformatics resources and databases, including PubChem and the Protein Data Bank (PDB), facilitate the acquisition of structural and pharmacological data. Case studies involving apigenin, luteolin, quercetin, and baicalin highlight how in silico approaches contribute to understanding their mechanisms of action, binding affinity, and therapeutic potential. The integration of computational strategies with in vitro and in vivo assays represents a cost-effective and time-efficient workflow to identify the most promising flavones for anticancer applications. Furthermore, current technological advances, including high-performance computing, are enhancing the accuracy and scope of in silico studies. Overall, this chapter emphasizes the value of in silico approaches as a first step in the rational design and selection of flavones for the development of innovative, targeted, and effective anticancer therapies.