Highlights What are the main findings? An interpretable machine learning framework (XGBoost-SHAP) reveals a "Greenness-Quality Paradox" in arid agro-ecosystems, where high vegetation cover masks secondary salinization and hydrological depletion. Ecological dynamics exhibit asymmetric driving mechanisms: improvement is predominantly anthropogenic (58.3%), whereas degradation is a deterministic process constrained by topography and climatic aridification. What are the implications of the main findings? The identified paradox challenges the prevailing assumption that increased vegetation is inherently beneficial and instead advocates management strategies that prioritize water-salt equilibrium over vegetation expansion. The quantitative thresholds established by the machine learning model inform the application of the Resist-Accept-Direct (RAD) framework, enabling a scientific balance between conservation objectives and hydrological sustainability.Highlights What are the main findings? An interpretable machine learning framework (XGBoost-SHAP) reveals a "Greenness-Quality Paradox" in arid agro-ecosystems, where high vegetation cover masks secondary salinization and hydrological depletion. Ecological dynamics exhibit asymmetric driving mechanisms: improvement is predominantly anthropogenic (58.3%), whereas degradation is a deterministic process constrained by topography and climatic aridification. What are the implications of the main findings? The identified paradox challenges the prevailing assumption that increased vegetation is inherently beneficial and instead advocates management strategies that prioritize water-salt equilibrium over vegetation expansion. The quantitative thresholds established by the machine learning model inform the application of the Resist-Accept-Direct (RAD) framework, enabling a scientific balance between conservation objectives and hydrological sustainability.Abstract The Arid Region of Northwest China (ARNC) functions as a critical ecological barrier for the Eurasian hinterland. To clarify the non-linear drivers of eco-environmental dynamics, a long-term (2000-2024) Remote Sensing Ecological Index (RSEI) time series was constructed and analyzed using an interpretable machine learning framework (XGBoost-SHAP). The analysis reveals pronounced spatial asymmetry in ecological evolution: improvements are concentrated in localized, human-managed areas, while degradation occurs as a diffuse process driven by geomorphological inertia. The ARNC exhibits low-level stability (mean RSEI 0.25-0.30) and marked unbalanced dynamics, with significant degradation (19.9%) affecting more than twice the area of improvement (6.5%). Attribution analysis identifies divergent driving mechanisms: ecological improvement (R2 = 0.559) is primarily anthropogenic (58.3%), whereas degradation (R2 = 0.692) is mainly governed by natural constraints (58.4%), particularly structural topographic factors, where intrinsic landscape vulnerability is exacerbated by human activities. SHAP analysis corroborates a "Greenness-Quality Paradox" in stable agroecosystems, where high vegetation cover coincides with reduced evaporative cooling and secondary salinization from irrigation, resulting in declining Eco-Environmental Quality (EEQ). A zero-threshold effect for grazing intensity is also identified, indicating that any increase beyond the baseline immediately initiates ecological decline. In response, a Resist-Accept-Direct (RAD) framework is proposed: direct salt-water balance regulation in oases, resist hydrological cutoff in ecotones, and accept natural dynamics in the desert matrix. These findings provide a scientific basis for reconciling artificial greening initiatives with hydrological sustainability in water-limited regions.