Accurately identifying medium- and low-yield farmlands (MLYF) and diagnosing their constraints are essential for targeted improvement of productivity and national food security. However, traditional evaluation is usually limited by coarse spatial resolution and high labor costs, and a methodological gap remains between large-scale MLYF classification and system constraints diagnosis. To address the current methodological gaps, this study developed a comprehensive framework to determine the spatial distribution of MLYF in northern China and clarify their key constraints. The framework combined the Spatio-Temporal Random Forest (STRF) algorithm with vegetation indices (VIs), climate, and soil data to delineate MLYF and uses interpretable machine learning to diagnose major constraints. The model showed high explanatory power and ensured the reliability of attribution results. The results showed that MLYF exhibited obvious spatial heterogeneity, accounting for 48.66% of the total cultivated land in the study area. These MLYF are primarily concentrated in the northwestern Loess Plateau (LP), the central Along the Great Wall (ATGW) region, and the peripheries of the Huang-Huai-Hai (HHH) Plain. In addition to spatial classification, our analysis revealed significant differences in constraint mechanisms: soil structural, nutrient, and salinization constraints predominantly restrict productivity in the HHH Plain, whereas water stress and soil erosion are the primary drivers of yield gaps in the LP and ATGW regions. These findings provide new data and insights for understanding the spatial heterogeneity of farmland quality in typical dryland agricultural regions in northern China, and offer a scientific basis for targeted land improvement and regional agricultural sustainability.