For the historical archives of time-series image resources from different sensors, the original photos often go unnoticed because of the lack of ground control points (GCPs). In this paper, we propose a GCP-free intelligent processing methodology for time-series archived historical aerial photos, which is based on cloud control photogrammetry and leverages airborne light detection and ranging (LiDAR) point cloud data. The key technologies discussed include the acquisition and accuracy evaluation of reference data, image matching, and aerial triangulation for subsequent three-dimensional (3D) modeling utilization. To validate our proposed method, we conducted experiments within the Fourth Ring Road area of Beijing’s Plain District, covering approximately 320 km2, using LiDAR data, the digital aerial photos obtained by AMC in 2017, the digital aerial photos obtained by UCXP in 2015, and the aerial films obtained by RC-10 in 1975–1990. On the basis of experimental results, it was concluded that a robust network of cloud control points with an interval of at least 1000 pixels could be established to replace traditional field GCPs, enhancing both the effectiveness and reliability of automated processing. Furthermore, experimental results demonstrated that this method allowed for the relaxation of orientation accuracy requirements for cloud control points from 0.35 to 0.6 pixels while maintaining the same level of accuracy. Even in cases where significant differences exist between the reference data and the archived photos, effective control can still be achieved by cloud control photogrammetry, with a threshold for eliminating gross errors set at 2–3 times the root mean square error.