<p>This review critically evaluates the impact of soil moisture variability on the performance of visible and near-infrared (Vis–NIR) spectroscopy for predicting soil organic carbon (SOC) content. Vis–NIR spectroscopy, a non-destructive and cost-effective method, has gained widespread use for assessing SOC due to its ability to rapidly generate data both in the laboratory and in the field. However, soil moisture is a significant confounding factor that introduces variability into spectral measurements. Moisture, by strongly absorbing near-infrared radiation, distorts the spectral features associated with SOC, leading to decreased accuracy in predictions. This review examines the physical principles underlying moisture interference and its effects on the soil reflectance spectrum. It also surveys various methodological approaches proposed to mitigate these effects, including spectral preprocessing, calibration transfer, moisture-based stratification, and robust modeling strategies. These techniques have shown varying degrees of success, with external parameter orthogonalization (EPO) and other advanced algorithms offering promising solutions. However, moisture-induced challenges remain an impediment to the reliable use of Vis–NIR spectroscopy for large-scale environmental monitoring and precision agriculture. The review highlights the necessity of developing improved correction techniques and the integration of multi-sensor approaches to enhance the accuracy and applicability of Vis–NIR spectroscopy for SOC prediction in the field.</p>