For the one billion sufferers of respiratory disease, managing their disease with inhalers crucially influences their quality of life. Generic treatment plans could be improved with aid of models that account for patient-specific features such as breathing and lung morphology. Therefore, we aim to develop and validate an automated computational framework for patient-specific drug deposition predictions. A novel image-processing approach is proposed to reconstruct 3D respiratory geometries from a single 2D X-ray. The 2D-to-3D image processing predicts airway diameter to 9% median error compared to ground truth segmentations, but produced some outliers (maximum error 33%). Validation of modelled deposition predicted 5% median error compared to experiments. We also develop a new method for data-driven stochastic modelling of particles in turbulent flows, which can be extended to general wall-bounded flows such as airways. The proposed framework is capable of providing patient-specific deposition measurements for varying treatments to determine which treatment would best satisfy the needs imposed by each patient (such as disease, airway morphology and breathing). Integration of patient-specific modelling into clinical practice as an additional decision-making tool could optimise patient treatment plans and lower the socio-economic burden of respiratory diseases.