Computational models of oral drug delivery commonly represent gastrointestinal physiology through regional averages, predefined transit compartments, or scenario-level adjustments. Such abstractions are useful, but they can obscure a central pharmaceutical reality: the gastrointestinal tract changes continuously after administration, and those changes alter the environment encountered by both the dosage form and the released drug. Transit, luminal pH, fluid availability, motility, food composition, bile-mediated solubilization, permeability, presystemic metabolism, and selected microbiome-mediated transformations do not act as independent constants. They form interacting, time-dependent processes whose consequences depend on compound properties, dosage-form behavior, administration conditions, and patient physiology. This article develops a proposed Dynamic Gastrointestinal State–Process Model that represents oral delivery as the co-evolution of regional physiological states and drug-product states. The model separates scenario initialization, segment-specific gastrointestinal trajectories, dosage-form transitions, luminal dissolution and precipitation, epithelial access, presystemic transformation, optional microbiome activity, virtual-population variability, and evidence qualification. Its central contribution is to shift computational interpretation from a fixed gastrointestinal background toward a conditional state-transition architecture in which local drug availability and systemic exposure emerge from competing and reversible processes. The proposal does not constitute a validated software platform, universal physiological representation, clinical prediction instrument, or regulatory framework. Its usefulness is conditional on compound-specific evidence, appropriate measurement resolution, qualified input distributions, identifiable mechanisms, and evaluation against both local and systemic observations. The framework may nevertheless help organize model development, sensitivity analysis, formulation comparison, food-effect assessment, and bioavailability prediction while making uncertainty and application boundaries explicit.