Super-resolution microscopy imaging technology surpasses the diffraction limit of traditional microscopes, thereby offering unprecedented detail and allowing observation of the microscopic world below this limit. This advancement remarkably promotes developments in various fields such as biomedical, cytology, and neuroscience. However, existing super-resolution microscopy techniques have certain drawbacks, such as slow imaging speed, artifacts in reconstructed images, considerable light damage to biological samples, and low axial resolution. Recently, with advancements in artificial intelligence, deep learning has been applied to address these issues, overcoming the limitations of super-resolution microscopy imaging technology. This study examines the shortcomings of mainstream super-resolution microscopy imaging technology, summarizes how deep learning optimizes this technology, and evaluates the effectiveness of various networks based on the principles of super-resolution microscopy. Moreover, it analyzes the challenges of applying deep learning to this technology and explores future development prospects.