IOTN grades are commonly used by orthodontic clinics to classify malocclusions. This system will classify malocclusions on the basis of the five main IOTN grades. Systems are trained to recognise patterns in large amounts of data through machine learning techniques. The algorithm developed for classifying IOTN combines convolutional neural networks (CNNs) with knowledge-based systems (KBSs). Image recognition and classification have been accomplished with CNNs. In AI, KBS recognises the expert's knowledge and helps with decision-making. KBS facilitates better decision-making and allows users to operate at higher levels of competence and consistency. One of the advantages of KBS is that people can access the information even when the experts are not available. ResNet-50, a trained Residual Network model, which has 50 layers deep, of CNN architecture, was used to develop the model. Aside from improving efficiency, artificial intelligence reduces the time it takes to perform certain tasks and reduces the likelihood of diagnostic errors due to the reduction of time-consuming tasks. In response to specific instructions, patients can upload images into the web-based system to perform an automated assessment of whether they are eligible for orthodontic treatment. It takes only seconds for the patient to receive a summary of their problem and an indication of how difficult it would be to treat it. It is now easy to follow patients during the retention period with the help of this remote provision, with patients providing images through apps rather than travelling for in-office visits. Aside from that, it also enables the scheduling of in-office appointments based on individualised treatment plans, resulting in a more efficient workflow. Machine learning is expected to assist dental professionals, dental students, and hygienists in identifying IOTN accurately. Possibly, there may be improved access to oral care and elimination of unnecessary appointments and reduction of in-office check-ups. It benefits the population, who can take oral photos using their smartphone or mobile device to conduct self-assessment remotely without going to clinics in which may take hours and even skipping the long waiting list for appointments.