While advanced models for groundwater level (GWL) forecasting have proliferated, their reliance on extensive auxiliary data and computationally intensive hyperparameter tuning limits real-world deployment, particularly in data-scarce arid regions. To bridge this, the present study introduces the synergistic autoregressive fuzzy-support vector machine (SAR-FSVM) framework for monthly GWL forecasting in the Birjand aquifer, Iran, and comparing its performance against conventional approaches, including multiple linear regression (MLR), support vector machine (SVM), and autoregressive SVM (AR-SVM). The proposed framework uniquely integrates three complementary components: the temporal dependency modeling of autoregressive (AR) models, the uncertainty quantification of fuzzy logic, and the nonlinear pattern recognition of SVMs within a single, end-to-end architecture that operates exclusively on historical GWL data. Using monthly data from 11 observation wells (1998-2017), the model was developed and validated. Results showed that SAR-FSVM outperforms conventional models, achieving a Nash-Sutcliffe efficiency coefficient (NSE) of 0.937 (training) and 0.907 (testing), a root-mean-square error (RMSE) of 0.228 m (training) and 0.267 m (testing), and a mean absolute error (MAE) of 0.166 m (training) and 0.190 m (testing). The fuzzy logic component effectively managed input variability, while SVM captured nonlinear dynamics. Analysis revealed that 1-3-month lags (p=3) were the most influential predictors, capturing the memory effect, where previous water levels influence current conditions, inherent in the Birjand aquifer's groundwater system. Overall, SAR-FSVM offers a data-efficient and accurate decision-support tool for sustainable groundwater management in an arid environment and holds strong potential for application to other hydrogeological systems worldwide.