Electronic commutator in Brushless DC motor (BLDC) drives decides the sequence of driving current through the stator windings based on the rotor position sensed by the three Hall position sensors. If any fault occurs in the Hall sensors, it will result in a faulty driving sequence of rotors and the motor will stall. This paper investigates an artificial intelligence-based Fault Diagnosis and Compensation (FDC) system for correcting up to two position sensor failures in trapezoidal back EMF BLDC motor drives. The proposed faulttolerant system is a simple, fast, and efficient method to diagnose and reconstruct the commutation signals using the existing processor-based commutator arrangements. An Artificial Intelligence algorithm is developed to estimate the correct switching sequence in the absence of up to two Hall sensor signals while the motor is running. This supervised learning network is advantageous as it is added as an additional control algorithm with the existing control algorithm for BLDC motor. The simulation and hardware results showcase the effective performance of the proposed algorithm for different Hall sensor fault conditions.