Regular inspection of tunnel ancillary facilities has become a cornerstone of sustainable tunnel operation and maintenance. Conventional manual inspections, however, are often plagued by low efficiency, elevated safety risks, and inherent subjectivity. In contrast, AI-driven diagnostic frameworks offer a transformative approach to enhancing detection precision and operational efficiency, providing a robust foundation for granular tunnel management. Given that current understanding of tunnel intelligent detection equipment and algorithms has not yet been systematized, a systematic review of recent research progress was conducted, based on a synthesis of relevant literature, from three perspectives: detection equipment, object detection algorithms, and defect detection algorithms. Regarding detection equipment, the architectural frameworks of mobile detection platforms were systematically introduced, and the advantages and limitations of two mainstream data acquisition techniques (high-resolution visible-light imaging and laser scanning) were thoroughly analyzed. Multi-sensor fusion technologies and tunnel positioning techniques were further explored. Concerning object detection algorithms, image processing techniques tailored to the special tunnel environment and data augmentation methods were summarized with typical case studies, and the performance of two-stage versus single-stage detection algorithms for tunnel ancillary facility target detection was compared, with emphasis placed on this comparison. With respect to defect detection algorithms, commonly adopted defect detection methods were reviewed, and the application of semantic segmentation and instance segmentation techniques to the refined assessment of facility defects was discussed. Finally, the core challenges confronting current intelligent equipment and technologies were summarized, including the difficulty of satisfying high-speed dynamic inspection requirements, the lack of standardized datasets, and insufficient model generalization capability. In response to these challenges, the outlook for future research was directed toward three major avenues: the development of integrated intelligent detection equipment tailored to high-speed dynamic scenarios, the exploration of generative AI for data augmentation, and the enhancement of model generalization ability and robustness under complex tunnel environmental conditions.