In this paper, a general regression neural network (GRNN) based controller is used to control the speed and thrust output of the linear induction motor drive. The field orientation principle is used to asymptotically decouple the motor speed from the secondary flux. The idea of model predictive control technique is used for the training of the proposed controller. The motivation for using this control strategy for training the GRNN based controller is to reduce the effect of the uncertainty due to motor parameters variation and load disturbance. This newly developed design strategy combines the advantage of the neural networks and MPC control techniques to provide robust performance and leads to a flexible controller with simple structure that is easy to implement. Digital simulations have been carried out to validate the effectiveness of the proposed scheme. The results of the proposed controller are compared with the corresponding one using the traditional PI controller. The results show that, the proposed technique has the ability to control successfully the speed and thrust of the linear induction drive in face of the motor parameters variation or load force disturbance.