The authors systematically review recent global literature on automatic monitoring technologies in tunnel construction, focusing on their applications and current research status. Technology analyses include measurement robotics, digital image processing and monitoring, radar monitoring, and fiber optic sensing and monitoring. The findings reveal that these technologies effectively capture dynamic changes in tunnel displacement, deformation, and structural stress, offering significant improvements in the precision and real-time capabilities of monitoring data. However, several challenges remain, including high equipment costs, dependence on specific technologies, low levels of automation, limited monitoring coverage, and the complexity of data processing and analysis. These limitations hinder the widespread adoption of automatic monitoring technologies in tunnel construction, leaving a considerable gap in achieving full automation and intelligent monitoring. The Internet of Things shows great promise for enabling intelligent transmission and centralized processing of monitoring data. Similarly, machine learning algorithms and digital twin technologies offer significant advantages in handling large volumes of complex monitoring data and enhancing the accuracy of early warnings. Nonetheless, challenges such as reliance on predictive models and high computational costs need to be addressed. Looking ahead, the development of automatic monitoring technologies in tunnel construction is expected to focus on digitalization, precision, and integration, paving the way for advancements toward higher levels of intelligence.