Artificial intelligence (AI) is increasingly being used in various areas of dentistry, including forensic dentistry, with a focus on the development of predictive models using different types of neural networks. In this study, two types of models were applied: feedforward neural net-works (FFNNs) and convolutional neural networks (CNNs), to simultaneously estimate gender and age based on orthopantomograms (OPGs), panoramic X-ray images of the jaw and surrounding anatomical structures. Although FFNNs are commonly used in classification tasks, they performed poorly in this study, achieving only 21.34% accuracy on the validation set. On the other hand, the CNN model developed in this study showed extremely high accuracy, reaching 99.74% on the validation set. It was found to be very effective in distinguishing gender and age across ten predefined categories. These findings clearly highlight the superiority of CNNs over FNNs for tasks involving medical image analysis and confirm their potential to enhance the speed and reliability of forensic identification processes.