Diffuse optical tomography (DOT) is a low-cost, non-radiative damage, deep detection in vivo optical functional imaging technology that uses near-infrared light to detect biological tissue optical structures. Due to the strong scattering, low absorption characteristics, and high spatial resolution of the biological tissue itself, the inverse problem of DOT reconstruction has serious ill-conditioned characteristics. The traditional inverse problem solution is mainly based on the algebraic iterative reconstruction method. With the development of artificial intelligence and the arrival of the era of big data, deep learning research has set off to reach another new climax. The inverse problem-solving method based on a deep learning network model is gradually used in the DOT reconstruction process. On the basis of combing the traditional DOT reconstruction algorithm, this manuscript focuses on the research progress of the latest deep learning for DOT reconstruction and provides reference for relevant research teams in this field.