The article explores the evolution and application of image inpainting, a technique designed to restore or reconstruct damaged or missing parts of images using advanced algorithms. Traditional methods relied on filling gaps with neighboring pixels, but these often failed with larger gaps. With the advent of deep learning, inpainting has seen significant advancements, allowing for automated, sophisticated restoration without human intervention. Techniques such as generative adversarial networks (GANs) and partial convolutions have been developed to address irregular hole patterns and enhance the authenticity of filled images. Methods like NVIDIA's edge-based approaches and pluralistic image completion offer multiple plausible solutions, highlighting the potential of machine learning to surpass conventional methods. Despite its progress, the effectiveness of deep learning for inpainting varies, with ongoing improvements anticipated as computational power increases. The article underscores the potential for these technologies to revolutionize digital restoration and image editing, offering new possibilities for both practical applications and creative exploration.