Excipient selection is a critical but laborious step in pharmaceutical preformulation because incompatibilities can derail development even when the active pharmaceutical ingredient appears chemically stable alone. Thermal analysis, hygroscopicity, and forced-degradation data contain predictive signals that are often generated during routine screening but are rarely unified in a quantitative decision model. Current compatibility screening is commonly treated as a manual, binary-decision process. This approach does not fully leverage historical information across drug–excipient pairs, which can lead to repeated experiments, delayed formulation choices, and overlooked incompatibility risks. The objective of this manuscript is to describe a conceptual machine learning model that predicts the compatibility of a drug–excipient binary mixture. The model would use descriptors derived from differential scanning calorimetry, thermogravimetric analysis, moisture sorption, and forced-degradation profiles. A gradient-boosted classification model would be developed using curated compatibility outcomes and engineered descriptors from thermal degradation, hygroscopicity, and stress-testing data. Input features would include glass-transition changes, melting-point shifts, enthalpy changes, temperature-dependent mass loss, moisture uptake indices, and forced-degradation readouts. Conceptually, the model would provide a compatibility probability together with interpretable feature attributions. Such output could help scientists rule out high-risk combinations earlier and reserve experimental compatibility studies for borderline or strategically important cases. A predictive compatibility tool of this type would accelerate early formulation development, reduce material waste, and embed data-driven decision-making into preformulation science. Its practical value would depend on careful curation, conservative interpretation, and continued expert oversight.