Drug repurposing can accelerate the transition from biological hypothesis to therapeutic evaluation by leveraging compounds with existing pharmacological knowledge; however, many computational predictions remain challenging to act upon because their underlying biological rationale is not explicit. Current repurposing models often treat drug–disease associations as black-box predictions, limiting their utility for biologists and clinicians who need to understand the pathways, targets, or transcriptomic relationships supporting a proposed indication. To address this, an explainable machine learning model can be designed to predict drug repurposing opportunities while providing transparent biological explanations for each prediction, highlighting influential transcriptomic signatures, pathway signals, and target proteins. Such a multi-modal model could integrate disease expression profiles, drug perturbation signatures, pathway enrichment features, and protein–protein interaction network proximity, with SHAP-based attribution and pathway-level attention decomposing predictions into interpretable biological components. Conceptually, the system would output a ranked set of drug–disease pairs alongside evidence narratives that specify the relevant pathways, target proteins, and directions of transcriptomic reversal, rendering each prediction biologically plausible. By providing interpretable insights, an explainable repurposing model would transform computational repositioning from a mere screening exercise into a hypothesis-generation framework, enabling scientists to prioritize predictions for experimental or translational follow-up.