High-performance field-oriented motor control requires accurate knowledge of flux and speed information. Furthermore, the elimination of sensors leads to reduced overall cost and size of the electric drive system and subsequently improving its reliability. So far, Speed sensorless control of induction motors has been faced with various techniques of speed estimation. However, the main drawbacks of these models are their insufficient performance at low speeds, along with sensitivity to parameters variation, which engenders an unsteady drive system. This paper proposes an effective sensorless indirect Field Oriented control scheme for an induction motor drive. The proposed speed observer combines artificial intelligence and model reference adaptive system (MRAS). This observer is associated with the control scheme as sensorless algorithms for rotor speed and flux estimation. This conjunction is intended to enhance the conventional MRAS performance especially in low-speed regions and reduce its sensitivity to noise and system uncertainties as well. Otherwise, the effectiveness of the proposed speed estimator is tested in simulation using Matlab/Simulink software environment. The control structure is checked in the low speed with load variation.