Image super-resolution (SR) addresses critical needs in medical diagnostics and geo-spatial analysis by enhancing low-resolution imaging data. While deep learning methods like Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) achieve high visual quality through adversarial training, they exhibit limitations in preserving anatomically critical edges in MRI scans and topographic features in satellite imagery. This paper presents a novel three-stage architecture that combines the generative capabilities of ESRGANof ESRGAN with a fuzzy inference system for edge optimization. The framework demonstrates a 7.8% improvement in the peak signal-to-noise ratio and 12% higher edge preservation scores compared to baseline ESRGAN on the DIV2K benchmark. The hybrid approach enables an interpretable edge enhancement through 27 fuzzy rules that govern gradient map optimization, addressing key limitations of purely data-driven methods.