The advanced technique of image inpainting has recently been developed in image processing for strong restoration. It transcends the mere filling of voids in old photographs and structural inconsistency corrections and largely finds applications in the restoration of dusty areas, blanks, and distorted parts of images. Most conventional restoration techniques such as image segmentation and autoencoders usually require manual marking of the sites that need repair. Such conditions limit the scalability or effect of these methods. Recently, advanced deep learning methods such as Conditional Generative Adversarial Networks (CGANs) have been found useful for provision of outputs as hyper-realistic without manual intervention even for very complex scenarios. CGAN-based architectures provide efficient methods for reconstructing the areas that get lost, while they keep a coherent structure and view. These models also improve the quality of images, being consistent with texture and colour through semantic conditioning within the discriminator. Performance measures prove efficacy, as the model boasts a clearly high PSNR of 27.06 dB and a near value of 0.72 SSIM, and produces very appealing visual results which are better than conventional methods.