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Artificial intelligence (AI) and cheminformatics are increasingly applied to Ayurvedic pharmacology. Curated phytochemical databases, target-prediction tools, protein interaction networks and machine-learning models make it possible to model the chemistry and actions of Ayurvedic drugs at a scale not previously possible. This review describes the main data resources and applications relevant to Dravyaguna, illustrates them with examples, and examines their limitations. IMPPAT 2.0 links 4,010 Indian medicinal plants to 17,967 phytochemicals and their therapeutic uses. PhytoMolecularTasteDB links Ayurvedic plants to taste-active molecules, and the Traditional Knowledge Digital Library has digitised classical formulations, a resource used mainly for patent protection. Machine learning has recovered the three extreme Prakriti types from phenotypic traits in a validated study. Advances outside Ayurveda show what is possible: a deep-learning model identified halicin, a structurally novel antibiotic, after screening large chemical libraries, and AlphaFold made accurate protein structure prediction widely available. Five limitations stand out. Databases describe what a plant may contain rather than what a classical preparation delivers. Predicted properties are treated as facts. Training data are biased towards well-studied compounds and targets. Models are rarely validated prospectively. And classical knowledge is not yet available in structured, machine-readable form. We recommend building structured digital representations of classical Dravyaguna properties, linking them to chemically characterised preparations, requiring prospective experimental validation of predictions, and adopting open, versioned data standards. Used in this way, AI could help test Dravyaguna concepts at scale rather than simply generate untested hypotheses.