Soil Organic Matter（SOM）, a vital component of the soil solid phase, provides essential nutrients for plant growth and serves as a key indicator of soil fertility. Recent advancements in remote sensing technology have introduced novel approaches for efficient SOM estimation and mapping, yet challenges persist due to environmental interference and data complexity. This review systematically examines the applications of multispectral and hyperspectral data in SOM inversion and mapping, alongside critical data processing methodologies. Comparative analyses demonstrate that laboratory-acquired spectral data under controlled conditions exhibit significantly higher model accuracy and robustness compared to field-collected data, attributed to stable measurement environments. Feature selection and extraction, particularly for hyperspectral datasets, enhance inversion precision by mitigating data dimensionality and multicollinearity. Ensemble modeling frameworks integrating machine learning and deep learning outperform single-model approaches by effectively characterizing the nonlinear complexity of soil systems. Multi-temporal datasets further improve predictive capabilities by incorporating seasonal vegetation dynamics and temporal evolutionary patterns. However, optical data remain susceptible to atmospheric disturbances, especially in cloud-prone regions such as southern China, while microwave remote sensing emerges as a complementary solution for its all-weather operability and topographic adaptability. Future research should prioritize multi-source synergy strategies, including optical-SAR synergies, multi-sensor platform integration, and physics-informed machine learning to address confounding factors like crop residue cover and soil moisture. Advanced preprocessing techniques, such as wavelet analysis and blind source separation, are essential for isolating soil-specific spectral signatures. Spatiotemporal modeling frameworks that integrate soil types, agronomic practices, and climatic variables will enhance prediction generalizability. Concurrently, developing interpretable artificial intelligence models and geographically adaptive spatial interpolation methods is crucial to ensure scientific rigor and global scalability. This study provides theoretical and practical insights for leveraging multi-source remote sensing in precision agriculture and sustainable land management.