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This work presents three automated pre-trained models to predict the difficulty of extracting the mandibular third molar using a dataset of 2414 panoramic radiography images based on preprocessed (shifted and rotated) from left and right mandibular third molar instances. In this research, we employed four distinct architectural models, namely VGG-16, VGG-19, MobileNetV2, and ResNet50 to identify the difficulty of removing a mandibular third molar. We categorized the dataset into four categories of complexity to help in categorization (Normal, Easy, Medium, and difficult). As a result, VGG-16, VGG-19, MobileNetV2 and ResNet50 had prediction accuracies of 81%, 82%, 79% and 44%, respectively. Findings affirmed that the proposed deep learning model using VGG-19 could be a good tool to predict the difficulty of extracting a mandibular third molar using a panoramic radiographic image.