Accurate estimation of actual evapotranspiration (ETa) is essential for sustainable water management and irrigation planning. This study presents a simulation–optimization framework, integrated with the Simple Algorithm for Evapotranspiration Retrieving (SAFER) model, for daily ETa estimation and applies it to a heterogeneous agricultural region with multiple crop types. Unlike conventional SAFER implementations that rely on linear regression or trial-and-error calibration, the proposed framework enables simultaneous calibration of model coefficients via a single optimization procedure while preserving the model’s nonlinear structure. Daily ETa was estimated using remote sensing inputs, including normalized difference vegetation ındex, land surface temperature (LST), surface albedo (αs), and meteorological data. The model was evaluated under two different training–testing scenarios to assess its robustness and generalization capability. Scenario I, which used only 38 satellite overpass days for calibration, achieved a test R² of 0.8195 and an overall R² of 0.8152. These results were consistent with Scenario II (test R² = 0.8249; overall R² = 0.8181), indicating stable predictive performance across different data partitioning schemes. The optimized coefficients remained highly consistent across scenarios (a ≈ 0.315, b ≈ −0.0012), demonstrating stable parameter convergence and robustness of the optimization process. Overall, the results indicate that reliable ETa estimation can be achieved even with limited calibration data, highlighting the practical applicability of the proposed framework for water management in data-scarce regions.