Significance The documentary sources for the political, economic, and social history of ancient Mesopotamia constitute hundreds of thousands of clay tablets inscribed in the cuneiform script. Most tablets are damaged, leaving gaps in the texts written on them, and the missing portions must be restored by experts. This paper uses available digitized texts for training advanced machine-learning algorithms to restore daily economic and administrative documents from the Persian empire (sixth to fourth centuries BCE). As the amount of digitized texts grows, the model can be trained to restore damaged texts belonging to other genres, such as scientific or literary texts. Therefore, this is a first step for a large-scale reconstruction of a lost ancient heritage. The main sources of information regarding ancient Mesopotamian history and culture are clay cuneiform tablets. Many of these tablets are damaged, leading to missing information. Currently, the missing text is manually reconstructed by experts. We investigate the possibility of assisting scholars, by modeling the language using recurrent neural networks and automatically completing the breaks in ancient Akkadian texts from Achaemenid period Babylonia.