Image colorization, the process of converting grayscale images to color, has seen significant advancements with the rise of deep learning techniques. While most work has focused on natural images, synthetic aperture radar (SAR) imagery remains predominantly grayscale, limiting its interpretability. SAR is widely used in remote sensing for applications, such as environmental monitoring and disaster management due to its ability to capture high-resolution images under various conditions. However, the absence of color makes it challenging for non-experts to distinguish features like water bodies, vegetation, and urban areas. The objective of this research is to create a deep learning-based framework for colorizing SAR images, thereby improving their interpretability and usability. The objective is to create a method that enhances SAR images without compromising structural details unique to the data. Our proposed approach utilizes convolutional neural networks (CNNs) and generative adversarial networks (GANs) to automatically generate colorized SAR images. The model is assessed using the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), and perceptual quality assessments to ensure plausible and effective colorization. By addressing challenges like speckle noise and geometric distortions, our framework demonstrates the potential to improve SAR image analysis, aiding in faster and more accurate decision-making across multiple fields.