A DC motor is a common actuator in process control systems that converts electrical energy into mechanical energy. This paper examined the performance of a deep learning-based neural network predictive controller (NNPC) for the analysis of speed control of a DC motor. The NNPC was designed and executed on MATLAB R2021b with license number 1075356 for the analysis. The proposed controller is based on a neural network that predicts the future behaviour of the motor system based on the current state and control inputs. The controller then uses this prediction to generate optimal control inputs that minimize the tracking error and improve the system’s performance. The results show that the proposed NNPC performs well in terms of accuracy, precision, and response time. The paper concludes that the proposed controller can be a viable option for the speed control of DC motor systems in various applications.