Nowadays, Permanent Magnet Synchronous Motors (PMSM) are used more and more widely due to their advantages over other types of motors, such as high efficiency, constant torque, higher power density, and wide speed range. Many studies on this motor have been carried out in the industry. This paper proposes an application for the PMSM motor to estimate the speed of the motor rotor using an extended Kalman filter (EKF). This also means that the motor is controlled without using a speed sensor, so the system has the advantages of reducing the cost of manufacturing encoders, less damage, increased reliability, and reduced size due to the absence of moving mechanical parts of the sensor. The estimated performance depends heavily on the parameters of the covariance matrices in the filter. In the paper, the filter parameters are optimized using the Cuckoo Search Algorithm (CSA). The simulation results of the proposed algorithm on the PMSM motor show its advantages over traditional methods.