Groundwater modeling in semi-arid regions presents significant challenges due to complex aquifer dynamics, limited data availability, and heterogeneous hydrogeological conditions. This study presents a comprehensive comparative analysis of three fuzzy expert system strategies for monthly groundwater level forecasting in the Al-Hsain Basin, Syria: localized models based on hydrogeographical grouping, a unified basin-wide approach, and an innovative behavioral clustering methodology. Using synchronized rainfall and temperature data from 35 monitoring wells over four years (2020–2024), we developed and evaluated fuzzy inference systems’ directional classification accuracy as the primary performance metric, categorizing groundwater level changes into rise, stable, and decline states rather than predicting continuous values. This choice reflects the qualitative nature of fuzzy expert systems and their suitability for groundwater management under data-limited conditions. The behavioral clustering approach achieved excellent overall performance with a mean accuracy of 0.74, outperforming localized models (0.71) and unified models (0.67). Behavioral clustering demonstrated effectiveness in 66% of wells, with individual accuracy improvements reaching up to 0.23, while reducing model complexity from five group-specific systems to three behaviorally coherent clusters. Localized models achieved optimal performance in 29% of wells where hydrogeological conditions aligned with spatial assumptions, whereas unified models provided consistent moderate performance across 89% of locations. The incorporation of lagged variables and seasonal indices in behavioral clustering models proved essential for capturing temporal complexity in semi-arid groundwater responses. Statistical analysis revealed lower intra-group variability in behavioral clusters (standard deviation 0.06–0.09) than in geographical groupings (0.08–0.14), confirming improved functional homogeneity through response-based organization. These findings indicate that fuzzy modeling strategy selection should be context-dependent, with behavioral clustering offering an effective balance between accuracy, interpretability, and generalization for regional groundwater management applications. The novelty of this work lies in isolating the effect of fuzzy system organization logic (localized, unified, and behavioral) on forecasting performance, robustness, and transferability, evaluated under an identical inference and time-series validation framework.