In this paper, a data-driven optimal output regulation control method using off-policy reinforcement learning is proposed for tracking control of discrete-time networked control systems with both linear disturbance and state dropouts in the feedback process. This method uses only measured online data to calculate control policies. First, in the environment where state dropouts exist, a restructured state of the system is established by using the Smith predictor. Then, under the output regulation framework, a data-driven optimal tracking control method using off-policy reinforcement learning is developed to calculate the feedback gain using only the measured data when dropout occurs. The connection with solving the output regulation problem is found in the process of solving the feedback gain. Based on the parameters related to solving the regulator equation in the process of solving the feedback gain, a model-free solution of forward gain is calculated. Finally, simulation results demonstrate the effectiveness of the proposed approach.