Underwater vision enhancement is essential for a wide range of marine applications like research, navigation, and surveillance, yet it is hindered by issues such as poor visibility, color distortion, and scattering effects. This paper presents a comprehensive review of existing literature on underwater image enhancement techniques, with a particular focus on the use of ’Super-Resolution Generative Adversarial Networks’ (SRGANs). We propose a novel SRGAN-based framework designed specifically for underwater image enhancement, incorporating adaptive preprocessing steps and customized loss functions to effectively address the unique challenges of underwater imagery. Experimental results demonstrate that our proposed methodology significantly outperforms current state-of-the-art approaches, achieving notable improvements in visual clarity, color restoration, and detail preservation. Our conclusions highlight the effectiveness of SRGANs in enhancing underwater vision, offering substantial potential for applications in underwater exploration, environmental monitoring, and marine biology research. Furthermore, we discuss future research directions to further refine these techniques for real-time deployment in underwater environments.