Global praise has been given to the use of deep learning techniques, particularly convolutional neural networks (CNNs), to perform medical imaging tasks like identification, estimation, and classification. Like conventional X-rays, panoramic dental X-rays have drawbacks but offer a comprehensive view of the oral cavity. These include variances in interpretation and poor image quality. The American Dental Association’s picture-based method is based on a completely automated system for classifying teeth and dental issues using panoramic X-ray images, which we discuss in this study. CNN-based semantic segmentation is used in the fundamental strategy, which is followed by techniques for image processing for tooth-level segmentation and problem detection. The system performed better than other models when assessed using the accuracy, precision, recall, and F1 score metrics.