The autonomous flight performance of unmanned aerial vehicles is significantly constrained by wind disturbances in practical applications such as logistics and inspection. Traditional filter-based methods rely on precise dynamic models and noise assumptions. When confronted with complex, time-varying wind disturbances, these approaches suffer from significant degradation in estimation performance due to unmodeled dynamics. In recent years, data-driven and hybrid methods have demonstrated tremendous potential. This paper aims to review research progress in UAV state estimation under wind disturbances, analyze the limitations of traditional Kalman filter-based methods in addressing wind disturbances, and explore the advantages of data-driven approaches in modeling and compensating for wind disturbances. It focuses on hybrid paradigms such as physically informed neural networks and model compensation learning, providing effective pathways for achieving highprecision, robust and generalized wind-resistant state estimation. Finally, this paper summarizes current technical challenges and outlines future research directions, particularly exploring the potential value of large language models in enhancing the intelligence and interpretability of state estimation systems.