To address airflow interference and inertial altitude dispersion during high-speed aircraft flight， a novel algorithm integrating cubature Kalman filter （CKF）， air data observation system， and inertial navigation system （INS） is proposed for real-time aircraft air data estimation through sensor data fusion. The algorithm utilizes nonlinear equations and models interactions among the inertial system， satellite positioning system， and air data system to integrate sensor data for computing aircraft velocity and altitude， followed by estimation of parameters such as angle of attack and sideslip. Experimental results demonstrate the high accuracy and stability of the proposed method in estimating airflow angle and Mach number.