The fusion of Raman and visible-near-infrared (VIS-NIR) spectroscopy provides a promising pathway for rapid and non-destructive soil analysis. However, conventional fusion strategies often fail to properly balance modality discrepancies and complementary information. To address this limitation, this study develops an adaptive Gated Ridge Regression fusion model (Fusion_GatedRidge) for predicting soil organic matter (SOM). A total of 246 soil samples collected from a dryland agricultural region in Shanxi Province were analyzed using laboratory Raman and VIS-NIR spectroscopy. After standard preprocessing, three baseline fusion frameworks-EarlyFusion_Ridge, AE_LatentFusion, and WeightedLate_Fusion-were implemented for comparison with the proposed gated fusion method. Under fivefold cross-validation, Fusion_GatedRidge achieved the best performance, with an R2 of 0.83, RMSE of 2.01 gkg-1, and RPD of 2.39. Compared with single-modality models, R2 increased by up to 18.6% and RMSE decreased by up to 23.0%. The gating mechanism dynamically adjusted the contributions of Raman and VIS-NIR features, leading to more stable predictions with residuals largely within -2 to 2 gkg-1. Overall, the proposed model demonstrates that adaptive modality weighting enhances the exploitation of complementary spectral information and significantly improves SOM prediction accuracy. These findings offer a concise and effective multimodal fusion framework for laboratory-based soil nutrient assessment.