Tis paper presents an advanced strategy combining fuzzy logic and artifcial neural networks (ANNs) for direct torque control (DTC) and broken-bar fault diagnosis in induction motors. More specifcally, a fuzzy-based controller is used to simultaneously minimize the stator fux and the electromagnetic torque ripples. A neural switching table is then proposed to achieve the interface inverter control. Besides, a closed-loop broken-bar fault detection strategy based on the Hilbert technique (HT) with the discrete wavelet transform (DWT) and ANNs is proposed. Te fault detection is performed by analyzing the induction motor’s stator current by using the combined techniques HT-DWT. Te efect of a broken-bar fault on the machine varies according to the number and position of the broken bars. Te neural detector was used in order to identify the number of broken bars through only one current measurement. Te efectiveness of the developed control has been verifed using MATLAB/Simulink and real-time simulation in OPAL-RT 4510. Obtained results show improved performances in terms of torque ripple minimization and stator current quality, evaluated, respectively, at 43.75% and 41.26% as well as a rigorous motor health monitoring.