This study evaluates CycleGAN's performance in virtual painting restoration, focusing on color restoration and detail reproduction. We compiled datasets categorized by art styles and conditions to achieve accurate restorations without altering original reference materials. Various paintings were degraded, including those with a yellow filter, to create effective training datasets for CycleGAN. The model utilized cycle consistency loss and advanced data augmentation techniques. We assessed the results using PSNR, SSIM, and Color Inspector metrics, focusing on Claude Monet's Nasturtiums in a Blue Vase and Hermann Corrodi's Prayers at Dawn. The findings demonstrate superior color recovery and preservation of intricate details compared to other methods, confirmed through quantitative and qualitative evaluations. Key contributions include employing CycleGAN for art restoration, model evaluation, and framework development. Practical implications extend to art conservation, digital library enhancement, art education, and broader access to restored works. Future research may explore dataset expansion, complex architectures, interdisciplinary collaboration, automated evaluation tools, and improved technologies for real-time restoration applications. In conclusion, CycleGAN holds promise for digital art conservation, with ongoing efforts aimed at its integration across fields for effective cultural preservation.