Abstract: As a significant source of greenhouse gases, coal mine methane (CMM) emissions pose a serious challenge to global climate change, with their monitoring and assessment having attracted significant and ongoing attention from researchers worldwide as a key focus of scientific investigation. Remote sensing, with its wide coverage, high spatial resolution, and real-time dynamic monitoring capabilities, has emerged as an essential tool for the monitoring and assessment of CMM emissions. Aiming at the problems such as insufficient multi-scale coverage, limited dynamic tracking ability and weak multi-platform collaboration in the monitoring of methane emissions in coal mines. The major technical platforms, methane concentration inversion methods, and emission quantification techniques are systematically reviewed, including satellite, aerial, unmanned aerial vehicle (UAV), and ground-based platforms. Methane concentration inversion methods such as the full-physics algorithm, CO₂ proxy method, and matched filter approach, as well as emission quantification techniques like Gaussian plume modeling, integrated mass enhancement, source pixel method, and cross-sectional flux approach, are also analyzed. The “Coal Mine Methane Ground-Unmanned-Satellite integrated monitoring system” (CMM-GUS) is proposed, which achieves “point-to-area” full-scale coverage monitoring of methane emissions in coal mining areas through multi-level collaborative observations by ground-based fixed stations, unmanned aerial vehicle (UAV) swarms, and hyperspectral satellites. The system integrates a Bayesian optimization inversion model and an “error propagation chain” quantification method to enable comprehensive monitoring across scales. After reading numerous articles on coal mine methane remote sensing monitoring technology, it is found that: Different monitoring platforms are suited for varying spatial scales and application scenarios, each offering distinct advantages. Satellite-based remote sensing is well-suited for large-scale emission trend analysis, while aerial and UAV platforms excel in medium- and small-scale high-precision monitoring. Ground-based observations provide critical data for the calibration and validation of multi-scale remote sensing results. Existing bottom-up and top-down technical frameworks for coal mine methane remote sensing monitoring, concentration inversion, and emission quantification are continuously evolving in terms of accuracy, applicability, and dynamic monitoring capabilities, offering a robust tool for preliminary methane emission assessments. Further integration of bottom-up and top-down methodologies can leverage their complementary advantages for mutual calibration. Current CMM remote sensing monitoring faces challenges, including difficulties in acquiring high-resolution data, the impacts of complex terrain and atmospheric conditions, uncertainties in remote sensing data, and insufficient multi-platform coordination. By establishing a multi-platform collaborative monitoring system, the limitations of traditional monitoring methods in spatial resolution and timeliness can be overcome. Multisource data fusion and model optimization enhance the accuracy of methane emission quantification, providing a scalable technical paradigm for global coal mine methane emission reduction.