Open-pit mining presents significant challenges due to steep topography, structural discontinuities, and rapidly changing surface conditions. UAV-based photogrammetry has emerged as an effective, cost-efficient solution for acquiring high-resolution spatial data in such complex environments. While numerous studies have explored specific applications of UAV photogrammetry in mining, a comprehensive synthesis addressing the full spectrum of technologies and operational strategies remains lacking. This review fills that gap by presenting an analysis of 33 representative studies published between 2017 and 2025, focusing on the integration of UAV photogrammetry, Structure-from-Motion (SfM), deep learning, and airborne LiDAR for monitoring and modeling open-pit mines. Key innovations include the use of oblique imagery, which provides superior vertical and occluded feature capture critical to mining operations. Quantitative comparison highlights that hybrid SfM–AI workflows achieve horizontal and vertical accuracies of ±2.9 cm and ±6.4 cm, respectively, with volumetric error rates below 5%. The application domains discussed include slope stability assessment, step-line and discontinuity extraction, volumetric monitoring, bench reconstruction, and reclamation analysis. Additionally, the paper examines the challenges of data noise, occlusion, and multi-temporal alignment, while proposing solutions to these issues. Finally, the review outlines future research directions, including real-time processing, AI-driven feature recognition, and geodynamic simulation, confirming that UAV photogrammetry is evolving into a cornerstone technology for intelligent, automated, and high-precision monitoring in sustainable open-pit mining.