Introduction: This study develops a deep learning hierarchical model for classifying otoscopic images to support clinical workflows for rural and remote Indigenous children. A multi-stage sequential analysis assesses otoscopic images for quality, external auditory canal (EAC) obstruction, tympanic membrane (TM) perforations, otitis media (OM), and conductive hearing loss (CHL). Methods: 10,000 ear assessment datasets were analyzed, including otoscopic images, audiology results, tympanometry, and nurse assessments collected over 10 years from the Royal Darwin Hospital and the Deadly Ears Program. Eight unique deep learning models were created using various pre-trained convolutional neural network (CNN) architectures, sequentially integrated into an ensemble for multi-stage otoscopic image analysis. Results: The model achieved an overall validation accuracy of 86%. In Stage 1, it screened images for quality with 99% accuracy (kappa = 0.97). Stage 2 detected EAC obstruction with 89% accuracy (kappa = 0.88). Stage 3 identified TM perforations at 92% accuracy (kappa = 0.80). Stage 4 distinguished discharging from non-discharging TM perforations at 71% accuracy (kappa = 0.61). Stage 5 differentiated otitis media from normal aerated ears at 92% accuracy (kappa = 0.79). Stage 6 classified acute otitis media versus otitis media with effusion at 84% accuracy (kappa = 0.70). Stages 7 and 8 predicted CHL at 84% accuracy (kappa = 0.67) and classified CHL severity at 75% accuracy (kappa = 0.60), respectively. Conclusions: The multi-stage AI model demonstrates robust performance in otoscopic image classification. Its sequential decision-making enhances clinical transparency and could serve as a valuable educational tool. However, limitations exist; for instance, 71% accuracy in distinguishing discharging TM perforations poses challenges for clinical decision-making. Future research should refine the model and incorporate more diverse datasets to improve diagnostic accuracy and clinical utility.