Groundwater protection is critical for sustainable water resource management, particularly in arid regions. However, current zoning methods show challenges such as data bias of expert-driven models and limited interpretability of machine learning models. To address these issues, using 16 hydrological datasets from Yulin City in northwest China, two methodological frameworks were constructed: one combining the traditional Analytic Hierarchy Process (AHP) with Geographic Information System (GIS), and the other combining machine learning methods with Principal Component Analysis (PCA) and Self-Organizing Map (SOM). Rather than proposing a novel hybrid model, this study establishes a comparative framework that serves as a prescriptive decision protocol: AHP-GIS provides a transparent, defensible basis for regulatory implementation, while PCA-SOM with SHAP analysis offers interpretable insights into data-driven patterns. The zoning results of these methods show high spatial consistency (81.10%) with some differences (18.90%). Both methods effectively captured medium to key protection zones, particularly in areas characterized by high groundwater yield, good water quality, and ecological sensitivity. SHAP analysis further explained methodological divergences: pollution resistance and mining intensity were the primary drivers of key protection zone in PCA-SOM (12.89%), contrasting with the expert-assigned priority to functional zone and water quality in AHP-GIS (20.06%). This dual-framework approach overcomes the limitations of individual methods by using AHP-GIS to address the black-box nature of machine learning for policy applications, while using PCA-SOM to counteract the subjective bias inherent in expert weighting. Comparisons reveal fundamental trade-offs between transparency and objectivity, pattern sensitivity, regulatory consistency and adaptability to complex spatial relationships. By providing a decision protocol for method selection based on specific management contexts, our findings offer actionable guidance for overcoming the limitations of current approaches.