This research introduces a minimum Hellinger divergence estimator for stochastic frontier models. The study establishes the strong consistency and asymptotic normality of this estimator under certain regularity conditions. Furthermore, it explores the robust characteristics of this approach. Through simulation, it showcases the robustness and efficiency of the estimator compared to alternatives like the minimum density power divergence method. Lastly, a real-data analysis is conducted to demonstrate the practical application of the estimator.