Tablet formulation failures are often driven by chemical and physical instability, which is affected by excipient selection, moisture uptake, manufacturing stresses, and storage conditions. Conventional stability prediction approaches—based on empirical rules, compatibility screens, and univariate kinetic models—offer limited insight into the complex interactions among multiple excipients, environmental stressors, and formulation variables. To overcome these limitations, this study introduces an explainable machine learning framework for predicting tablet shelf-life and degradation tendencies, leveraging excipient properties, moisture sorption characteristics, and accelerated stability data. The framework employs tree-based ensemble models, such as gradient-boosted trees or random forests, trained on formulation records encompassing excipient descriptors, moisture uptake parameters, process variables, and stability endpoints. SHAP analysis is then applied to break down each prediction into contributions from individual formulation and storage features, allowing the model to not only identify formulations at risk of instability but also elucidate underlying causes, such as hygroscopic fillers, moisture-sensitive drugs, insufficient moisture protection, or interactions between humidity and excipient chemistry. By linking predictions to actionable formulation levers, this explainable approach supports insight-driven optimization, enhancing product robustness and streamlining tablet development beyond traditional trial-and-error methods.