Severe drug–drug interactions remain a major source of preventable patient harm and healthcare burden. Conventional systems often rely on static pairwise interaction tables and therefore struggle to reflect the complexity of real-world polypharmacy. Existing prediction tools insufficiently integrate pharmacokinetic mechanisms, spontaneous adverse event evidence, and the patient’s full medication context. This gap can contribute to both missed severe interactions and excessive low-priority alerts. The objective is to develop a conceptual machine learning model that predicts the severity of a potential drug–drug interaction for a given drug pair or multi-drug regimen. The model would use features derived from polypharmacy exposure, pharmacokinetic pathway overlap, and real-world adverse event reports. A gradient-boosted classification model would be constructed using structured predictors that represent concomitant drug burden, CYP450 and transporter pathway overlap, and pharmacovigilance signal measures from FAERS or VigiBase. The target label would represent serious clinical consequences such as hospitalization, death, or other medically significant outcomes. Conceptually, the model could identify severe interactions that are incompletely represented in standard compendia, particularly when risk emerges from multi-drug exposure rather than a single pairwise mechanism. It would also provide an interpretable decomposition of risk across pharmacokinetic, polypharmacy, and real-world evidence components. A severity-focused predictive model could support safer prescribing by prioritizing clinically urgent interactions for review. Such an approach could reduce alert fatigue by distinguishing high-concern interaction signals from lower-severity flags.