Dust storms in arid and semi-arid regions present a critical environmental challenge, arising from complex interactions between climatic patterns, landform features, and surface conditions. The Sistan and Baluchestan Province in southeastern Iran is a global dust hotspot, yet the quantitative impact of its diverse environmental drivers remains poorly understood. This study quantifies these relationships by evaluating the influence of 18 environmental variables on dust occurrence over a 20-year period (2003-2023). To achieve this, we developed a robust framework combining six machine learning algorithms (Logistic Regression, KNN, SVM, MLP, Random Forest, and XGBoost) with a rigorous hybrid feature selection strategy (VIF-RFE). While ensemble models (Random Forest and XGBoost) and KNN demonstrated superior performance (AUC similar to 0.89), capturing the nonlinear nature of dust generation, the Shapley Additive exPlanations (SHAP) analysis revealed a clear hierarchy of drivers. Topographic features, specifically elevation, were the dominant broad-scale predictor, followed closely by hydro-climatic factors (precipitation and NDMI) and soil chemical properties (Salinity/VSSI). In alignment with the region's hyper-arid characteristics, vegetation indices (e.g., NDVI, MSAVI) showed minimal predictive power. As over 93% of the landscape constitutes naturally barren soil or sparse cover, the vegetation falls below the critical threshold required to act as a significant wind buffer, leaving abiotic drivers (soil moisture and salinity) as the primary determinants of dust generation. These findings highlight that mitigation strategies must prioritize soil moisture preservation and salinity stabilization over isolated revegetation efforts, providing a scientific foundation for targeted management in hyper-arid regions.