<p>Traditional clothing design relies on manual drawing or computer graphics software to complete the design. Machine learning can automatically extract design elements and style features through learning and analyzing a large amount of data, providing intelligent design suggestions and solutions for designers, thereby improving design efficiency and quality. The research adopts depth camera sensor technology to obtain the three-dimensional position information of the human body and uses machine learning algorithms to process and analyze the depth data. In terms of image segmentation, by integrating fuzzy clustering algorithms and deep learning algorithms, precise segmentation and recognition of clothing graphics can be achieved. Build a knowledge base of clothing experts through machine learning algorithms to provide intelligent design suggestions and solutions for designers. In the experiment, the segmentation effects of different algorithms were evaluated. The experimental results show that the image segmentation algorithm based on machine learning performs well in terms of segmentation accuracy and robustness, especially when dealing with complex scenes and noise, it can achieve more accurate segmentation effects. Compared with traditional methods, machine learning algorithms can significantly improve design efficiency, shorten the design cycle, and provide more personalized design suggestions. The RSNI_FCM algorithm performs best in terms of image segmentation quality and noise robustness, and can better balance noise robustness and detail retention ability. By combining depth camera sensors with image segmentation algorithms, this system can achieve more accurate human body measurement and clothing positioning, helping designers understand human body structure and needs more precisely. Machine learning algorithms can also enhance design efficiency and quality, reducing the time and cost required by traditional fitting models through virtual fitting and real-time adjustments.</p>