Due to the cranial suture ossification in conjunction with age, the degree of fusion between the neighboring skull plates has been used as a predictor for estimating age, based on skeletonized human remains. So far, this involved the use of subjective ranking techniques, which rely entirely on the human opinion for assessing the degree of fusion. Such methods have shown to require a lot of human resources and not produce sufficiently accurate results in comparison with other age estimation methods. This project manages to entirely automate the process of age estimation based on cranial suture analysis and based on the available sample, suggests a significant improvement in the error of produced age estimates. This is achieved with the help of computer algorithms and deep learning.