This research introduces a novel method for image enhancement and repair using Generative AI - Stable diffusion model. Unlike traditional techniques, it selectively masks and replaces areas to adjust aspect ratio while preserving details. Inspired by digital artists, it advances visual creation. Primary focus is on image inpainting, crucial for restoration, editing, and synthesis. Our approach merges deep learning in Generative AI with Stable diffusion, enhancing quality and stability of inpainted images. Stable diffusion models simulate gradual information spread across missing areas, yielding coherent outcomes. Integration of generative AI enables understanding of image structures, aiding realistic content generation. Inpainting guided by stable diffusion ensures seamless transitions and contextual coherence preservation. Adaptive contextual understanding fills missing regions based on global and local context. Evaluation criteria assess image quality, coherence, and similarity to the original. Results advance inpainting techniques for real-world applications, addressing challenges with improved visual quality and contextual consistency.