This paper explores the advanced methodology of colorizing Synthetic Aperture Radar (SAR) images using deep learning techniques. By integrating grayscale SAR data with optical RGB information through models such as U-Net and GAN, the study aims to generate visually meaningful and realistic colorized images. The proposed framework leverages key SAR features—including polarization, backscatter intensity, and interferometric data—enhancing interpretability for applications like urban mapping, environmental monitoring, and disaster management. This literature survey provides a comprehensive review of the base study along with fourteen additional research contributions, underscoring the evolution, challenges, and prospects of SAR image colorization.