Oral bioavailability is a key determinant of whether a drug candidate can be developed as a practical oral medicine. It reflects the combined influence of molecular structure, intestinal permeability, metabolic extraction, and formulation-dependent release or solubilization. Many prediction approaches rely on simplified molecular rules or isolated in vitro measurements. Such approaches may overlook the multi-modal data streams routinely generated during discovery and development, including permeability assays, metabolic stability studies, and formulation attributes. The objective of this predictive modeling article is to define a machine learning framework for estimating oral bioavailability from molecular, permeability, metabolism, and formulation features. The model is intended to support early ranking of compounds and formulation strategies rather than replace definitive pharmacokinetic studies. A gradient-boosted tree model would be trained on curated oral bioavailability measurements linked to chemical structures, in vitro permeability values, intrinsic clearance estimates, and formulation descriptors. Feature engineering would convert heterogeneous experimental and categorical information into a harmonized input vector suitable for interpretable prediction. Conceptually, the model could predict oral bioavailability by learning non-linear relationships among molecular descriptors, epithelial transport surrogates, metabolic liability, and formulation class. It would also be expected to generate interpretable feature-attribution patterns and uncertainty estimates for risk-based decision making. A holistic, data-driven bioavailability model could accelerate candidate selection and formulation design in early drug development. Its greatest value would lie in integrating routinely available evidence into a single transparent prediction workflow.