Pharmacokinetic drug interactions can significantly alter drug exposure, particularly when a perpetrator drug inhibits or induces enzymes and transporters responsible for a victim drug’s clearance, and predicting the resulting fold-change in exposure requires consideration of pathway contribution, inhibitor potency, transporter involvement, and clinically relevant dose. Existing prediction tools range from simplified mechanistic equations to complex physiologically based pharmacokinetic simulations; while valuable, their outputs can be difficult for clinicians and regulators to interpret when multiple enzyme, transporter, and dose-related mechanisms act simultaneously. This article proposes an interpretable machine learning framework for predicting the AUC ratio of a victim drug when co-administered with a perpetrator drug, aiming to transparently attribute each prediction to enzyme inhibition, transporter effects, victim-drug disposition features, and perpetrator dose. A gradient-boosted tree model would be trained on curated clinical DDI evidence using features that encode CYP and transporter inhibition constants, fraction metabolized or transported, and dose-related exposure surrogates, with SHAP explanations decomposing each prediction into additive feature contributions reviewable at the level of a single interaction. Conceptually, the model would provide both a predicted interaction magnitude and a mechanistic explanation, indicating whether the prediction is primarily driven by strong CYP inhibition, transporter effects, dose-related exposure, or victim-drug pathway dependence, thereby creating a transparent audit trail. By connecting predicted exposure changes to mechanistic features, such an interpretable DDI prediction model could enhance confidence in computational pharmacokinetic risk assessment and support clinical decision-making, prescribing guidance, and regulatory review.