Researchers can now identify dynamic activities in living cells at the nanoscale with remarkable temporal and spatial resolution because of the advancement in fluorescent super-resolution imaging. Traditional super-resolution microscopy requires high-power lasers or numerous raw images to rebuild a single super-resolution image, limiting its applications in live cell dynamic imaging. In many ways, deep learning-driven super-resolution imaging approaches break the bottleneck of existing super-resolution imaging technology. In this review, we explain the theory of optical super-resolution imaging systems and discuss their limitations. Furthermore, we outline the most recent advances and applications of deep learning in the field of super-resolution imaging, as well as address challenging difficulties and future possibilities.