Intelligent flow field modeling methods, by integrating the strengths of deep learning in feature extraction and dynamic response prediction with architectural innovation potential in MDO (multidisciplinary design optimization), have emerged as research hotspot for achieving efficient modeling of complex flow systems and enhancing high-dimensional performance. The state-of-the-art in intelligent flow field modeling was systematically reviewed from two perspectives: data-driven approaches and physics-constrained methodologies. Three critical challenges, including acquisition of high-fidelity data, representation of complex boundary geometries, and establishment of robust physical constraints, were identified. Furthermore, a joint modeling framework that integrated aerodynamics and multidisciplinary coupling effects was expected to revolutionize the next generation of aircraft MDO paradigm through multi-scale physical information embedding and adaptive optimization mechanisms. A new idea for the deep integration of data knowledge and physical mechanisms was provided, aiming to inspire interdisciplinary innovations for intelligent flow field modeling in aerospace and other fields.