Tunnel maintenance is crucial for averting safety incidents triggered by tunnel structural issues. Effective maintenance depends on thorough and accurate detection of tunnel diseases. Traditional methods of tunnel inspection rely on manual detection, which are hindered by factors like inadequate tunnel illumination and limited inspection time, leading to inefficiencies and inaccuracies. To tackle these challenges in tunnel disease detection, a subway tunnel disease monitoring system has been devised, taking into account the uniformity of tunnel structures and the fixed nature of track lines. This system integrates laser scanning technology with photogrammetry, employing laser scanners to capture three-dimensional point cloud data of tunnels and multi-line array cameras to obtain tunnel imagery. Laser trackers are utilized for calibrating the spatial coordinates of cameras and scanners to ensure a unified coordinate reference system. Adopting a distributed software architecture, the system develops data acquisition software for sensor communication and data storage. In addition, the system uses an external trigger mechanism to synchronize the sensor data acquisition rate with car speed. Finally, a data management platform, built upon the Cesium framework, is employed for the organization and manage of tunnel data. The system is applied to a tunnel to verify data accuracy. The experiment shows that the rail tunnel disease detection system can detect cracks with a width of 0.2mm and misalignment with a size of 0.5mm in the actual tunnel, and has the disease detection ability of subway tunnel. Finally, by applying the data collected in the experiment to tunnel crack detection, misalignment detection, deformation detection, etc., and locating the detected diseases, the practicability of the rail tunnel disease detection system is proved, and a reliable solution is provided for the subway tunnel disease detection.