The integration of advanced machine learning techniques, particularly large language models (LLMs) and geometry-aware neural networks, is revolutionizing traditional engineering tasks. This trend is exemplified by the use of LLMs for intelligent decision-making in parametric shape optimization, which has demonstrated faster convergence and improved agreement with benchmark solutions. Additionally, geometry-aware message passing neural networks are enhancing aerodynamics modeling by effectively incorporating geometric structures. Notable advancements include the GeoMPNN framework, which integrates a hybrid Polar-Cartesian coordinate system with message passing schemes, significantly improving modeling accuracy and efficiency. These innovations not only enhance the performance of existing methods but also open new research avenues by intertwining machine learning with engineering design, promising more innovative and efficient solutions in the future.