Abstract: In recent years, with the constant rise in the number of infertility patients, the rapid and accurate assessment of sperm quality has become a key challenge in reproductive medicine. Currently, traditional methods for sperm classification suffer from limitations in efficiency, subjectivity, and cost-effectiveness, and hence fail to meet clinical needs. With the introduction of artificial intelligence (AI), innovative approaches have been offered to address these issues. Herein, we systematically reviewed the latest progress in AI applications in the field of male infertility, focusing on the role of AI in the assessment of sperm concentration, motility, morphology, DNA fragmentation index, and the treatment of non-obstructive azoospermia. It has been reported that AI technologies, such as convolutional neural networks, demonstrate high accuracy and efficiency in assessing sperm concentration and motility. Particularly in morphological analysis, the performance of AI has been validated in multiple studies, significantly enhancing objectivity and clinical utility. However, the assessment of DNA fragmentation index remains underexplored due to the lack of support in advanced imaging technology, with only a few models showing promise. Additionally, AI significantly improves sperm detection rates in modified testicular sperm extraction through AI-driven image recognition, offering a breakthrough in the treatment of patients with non-obstructive azoospermia. We also discussed the application of natural language processing technology in patient pre-consultation and follow-up, such as automated data collection and intelligent tracking systems, which demonstrate AI’s potential to optimize medical workflows. In the future, with the accumulation of high-quality datasets, algorithm optimization, and advances in imaging technology, the application of artificial intelligence is expected to enable multi-dimensional comprehensive screening and play a greater role in the diagnosis and treatment of male infertility.