The BLDC motors are extensively used in aerospace, electric vehicles, medical equipment, etc., owing to their outstanding speed–torque characteristics. However, BLDC motors require controllers to control the speed, torque and output power based on the application. The PID, fuzzy and ANN-based controllers that are used for the control of BLDC motor have limitations due to their design complexity and implementation. In this paper, an adaptive neuro-fuzzy inference system (ANFIS) has been developed to control the speed of BLDC motor drive and the simulation results are investigated and compared with existing control techniques under the specified operating conditions.