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When there is obvious turning behavior in the trajectory of the drone, if the effective coupling between attitude angle and response curve cannot be achieved, the aircraft's anti-interference ability will decrease, thereby reducing the flight quality of the drone. To address the above issues, a machine learning based cascade linear autodisturbance rejection control system for unmanned aerial vehicles is designed. Connect the cascade linear tracking differentiator, extended state observer, auto-disturbance rejection unmanned aerial vehicle attitude controller, and travel position controller as needed to complete the design of the drone cascade linear auto-disturbance rejection control hardware system. Establish a machine learning model adversarial network, solve the drone cascade linear dynamic motion formula, combine relevant motion data, and complete the drone cascade linear dynamic performance analysis based on machine learning. Define the cascade linear pose coordinates, calculate the specific auto-disturbance rejection control conditions by deriving the auto-disturbance rejection motion node matrix, and achieve auto-disturbance rejection control of the drone cascade linear pose. Combined with relevant application component structures, complete the design of the drone cascade linear auto-disturbance rejection control system based on machine learning. The experimental results show that under the action of a machine learning control system, the coupling error between the pitch angle and roll angle of the two types of attitude angles and the standard response curve does not exceed 10%. Even in the case of obvious turning behavior in the travel trajectory, the application of this system can achieve effective coupling between the attitude angle and response curve, effectively ensuring the anti-interference ability of the unmanned aerial vehicle.