Intelligent flow field modeling methods, by integrating the strengths of deep learning in feature extraction and dynamic response prediction with architectural innovations in multidisciplinary design optimization (MDO), have emerged as a core driver for achieving efficient modeling of complex flow systems and enhancing high-dimensional performance. This fusion paradigm not only strengthens the coupling between data and physics but also provides aerodynamics design with computationally efficient and physics-consistent solutions through multi-objective optimization mechanisms. This paper reviews recent advances in intelligent flow field modeling from two perspectives, namely data-driven approaches and physics-constraint approaches. Key challenges hindering progress include the acquisition of high-fidelity datasets, geometric feature representation, and robust physical constraints. Furthermore, we propose that joint modeling integrating aerodynamics with multidisciplinary coupling effects holds transformative potential for next aircraft MDO paradigms. By bridging data-driven flexibility and physics-based rigor, this work aims to inspire interdisciplinary innovations in intelligent flow field modeling.