Super-resolution microscopy has broken through the diffraction limit of conventional optical microscopy, providing a crucial tool for high-resolution optical imaging at the sub-cellular scale. This breakthrough has significantly advanced research in frontier disciplines such as cell biology, neuroscience, and pathology. Among various super-resolution techniques, structured illumination super-resolution microscopy (SIM) technology has emerged as a vital method for studying the dynamics of fine structures in live cells, owing to its rapid imaging capability, low phototoxicity, and excellent compatibility with fluorescent probes. With the rapid development of artificial intelligence, deep learning has injected new momentum into SIM technology. Deep learning-enhanced SIM has achieved groundbreaking progress in reducing phototoxicity, increasing imaging speed, improving resolution, and expanding functional applications, greatly broadening its scope. This review systematically summarizes recent advances in deep learning-driven SIM technology and provides a perspective on future developments in the field.