Paddy field sustainability is essential for food security in paddy-dependent countries but is shaped by complex and spatially heterogeneous interactions among environmental, social, and economic factors. Conventional land-use models often assume spatial stationarity, limiting their ability to capture localized dynamics. This study proposes a spatially explicit analytical framework by integrating Geographically Weighted Logistic Regression (GWLR) with multi-layer probabilistic surface analysis to model and classify paddy field sustainability. The framework is applied in Indramayu and Majalengka Regencies, Indonesia, using 400 stratified samples (53% paddy, 47% non-paddy) and 10,000 prediction points. Results show that GWLR outperforms global models, explaining 25.5% of deviance compared to 7.4% for logistic regression. More importantly, it reveals spatially non-stationary relationships: environmental variables exhibit relatively continuous effects, while social and economic variables show strong local heterogeneity. By transforming local coefficients into integrated probability surfaces, this study introduces a novel typology distinguishing stable paddy fields, vulnerable areas, and spatially differentiated sustainability conditions. This approach moves beyond aggregate accuracy metrics and highlights the importance of spatial context in land-use analysis. The proposed framework offers a transferable method to support place-based agricultural protection strategies in complex geographical systems.