Dental remains are one of the most resistant elements to blunt force, fire, bacterial decomposition, and other external factors, making them a prime candidate for forensic analysis. The forensic analysis aims to identify human remains, with the estimation of age and assessment of sex at the forefront. Current methods of forensic odontology are based on time-intensive, precise manual measurements paired with reference tables and atlases, which can lead to human error. Artificial intelligence and computer vision research shows unprecedented success in many domains, including medical image analysis. This research explores the intersection of deep learning-based methods for computer vision and forensic odontology. Two neural network architectures are proposed, one based on state-of-the-art architectures used as feature extractors paired with attention, and one fully built around the BiFPN module. The hyperparameter space is exhaustively examined, and the best models are compared to the current state of literature. Models are developed for age and sex estimation, tooth detection and type determination, and segmentation of tooth and status. Imperfect teeth with alterations are not excluded, and their impact is extensively examined. The proposed methods perform as well or better than current methods in literature while being fully automated, reproducible, and multiple orders of magnitude faster. The contributions of the thesis are deep learning-based models for sex assessment (classification), age estimation (regression), and region-proposal for tooth status assessment from dental x-ray images.