The rapid advancement of Artificial Intelligence (AI) has significantly impacted the design of Computer Numerical Control (CNC) machine tools, with AI-generated Content (AIGC) playing a pivotal role in driving innovation. Traditional CNC machine design methods typically focus on functionality and efficiency. However, AIGC enables the creation of more creative, user-centered, and customizable designs, signaling a shift towards smarter and more innovative production. This study explores the application of AIGC in CNC machine tool styling design, and proposes a CNC machine tool design workflow, covering dataset creation, model training, and optimization. A diverse dataset of machine tool images was collected and processed using AI tools such as Dreambooth and BooruDataset Tag Manager. The generated design concepts were trained using a stable diffusion model and optimized by adding control tags to encourage greater creativity and innovation. Results demonstrated that AIGC can inspire new, flexible, and aesthetically appealing designs. However, challenges such as improving image quality, ensuring accurate alignment with textual descriptions, and addressing intellectual property issues remain. The study recommends enhancing training algorithms, image enhancement techniques, and diversifying training data to overcome these obstacles. In conclusion, AIGC has great potential to revolutionize CNC machine tool design, offering opportunities for customization, sustainability, and enhanced user experience. The integration of AIGC into the design process is expected to drive innovation and increase the competitiveness of CNC machine tools in the market, setting the stage for future advancements in intelligent manufacturing.