Gallbladder diseases affect a significant portion of the global popula tion, ranging from gallstones and cholecystitis to carcinoma. Accurate diagnosis is critical but often delayed due to overlapping symptoms and limitations in ul trasound imaging interpretation. This study proposes an advanced multi-class classification system leveraging deep learning techniques to identify nine distinct gallbladder disease types using ultrasound images. The methodology integrates preprocessing techniques such as CLAHE and active contour segmentation to enhance image quality and isolate regions of interest. A hybrid ensemble model combining VGG16, ResNet152, and a custom CNN achieved superior perfor mance with 99.8% accuracy and a Cohen Kappa score of 99.8%. Transfer learn ing, feature fusion, and ensemble strategies were employed to improve robust ness and generalization. Explainable AI (XAI) techniques like Grad-CAM and LIME were incorporated to provide interpretable visualizations of the model’s predictions, aiding clinical decision-making. The system was trained on the UI DataGB dataset containing 10,692 annotated ultrasound images, ensuring high reliability across diverse gallbladder conditions. Comparative benchmarking demonstrates that the proposed model outperforms existing systems in accuracy and classification depth. This research contributes a scalable, interpretable AI driven diagnostic tool that enhances early detection and management of gallblad der diseases while addressing challenges in medical imaging variability.