Inhaled drug delivery is central to treating respiratory diseases because it can place therapy near the intended pulmonary site of action. However, predictable lung deposition remains difficult because particle properties, device performance, and patient breathing behavior interact in non-linear ways. Current development workflows often treat in-vitro aerosol testing and in-vivo imaging as separate sources of evidence. This separation limits the ability to forecast regional deposition from the combined effects of formulation, device, and patient variables. The objective is to develop a machine learning model that predicts regional lung deposition of inhaled drugs. The model is designed to integrate particle aerodynamic parameters, device resistance characteristics, and patient inspiratory flow profiles. A gradient-boosted regression model is conceptually trained on combined cascade impaction outputs, device resistance descriptors, inspiratory flow waveforms, and deposition fractions from imaging or computational simulations. The model outputs predicted deposition for clinically relevant lung regions. Conceptually, the model could forecast the fine-particle dose reaching the central and peripheral airways for a given patient profile and inhalation device. It could also identify whether particle size, device resistance, or inspiratory flow is expected to dominate the deposition outcome. Such a model could accelerate inhaled product development by supporting virtual bioequivalence assessment and personalized device–formulation selection. It would provide a structured bridge between aerosol characterization, patient physiology, and regional lung delivery.