<p>The deterioration, damage, and fading of the ceramics are common problems. Most traditional restoration methods are time-consuming and limited in scope. Thus, Artificial Intelligence (AI)-based image processing technology holds great potential for enhancing and preserving such artworks. To generate anAI-based technique utilizing a Secretary Bird Search fine-tuned Scalable Generative Adversarial Networks (SBS-SGAN) to enrich ceramic artworks through better visual quality and restoration of damaged parts using image processing for digital preservation. Images of several ceramic artworks have been gathered from museums and digital archives, varying in periods, styles, and conditions. The images are resized to uniform dimensions, followed by applying a Gaussian filter for noise reduction and detail enhancement. ResNet architecture is utilized for feature extraction, capturing intricate details of ceramic textures and patterns. The method utilizes scalable GANs optimized by the SBS algorithm for high-quality image restoration and enhancement for different ceramic pieces. The test scenario of the proposed method was implemented in Python and the results finally led to significant improvements. The model enhances the quality of both damaged and undamaged ceramic artworks. AI-driven methods provide promising solutions for the restoration of ceramic artworks, SBS-SGAN, which outperforms existing models in terms of accuracy (97.2%). The model demonstrates effectiveness in enhancing image quality, providing insights into future application scenarios to preserve cultural heritage.</p>