The F50 SailGP catamaran is one of the most advanced vessels in competitive sailing. This study presents a multidisciplinary approach combining CAD modeling, additive manufacturing, and deep learning for the digital reconstruction and recognition of the F50. Using CATIA V5, a detailed virtual model was developed based on publicly available visual data, followed by the fabrication of a scaled prototype using FDM technology. The CAD models were segmented for printability, and post-processing ensured dimensional accuracy and structural integrity. In parallel, a YOLO-based computer vision model was trained on a manually labeled dataset of 100 images to identify the F50 in diverse visual contexts. The model achieved 83% precision, demonstrating the feasibility of automated recognition despite limited data. This work highlights the potential of integrating engineering design, rapid prototyping, and AI tools in the analysis and dissemination of high-performance sailing technologies, offering a foundation for future developments in configuration-specific recognition and automated design validation.