Whenever DC motor operates at varying speeds and is expected to rotate in two directions with high precision, it becomes essential that uncertainties in the form of external disturbances and load variations in the system be considered. In such systems, the use of fixed-gain Proportional Integral Derivative (PID) controller alone is suboptimal as the controller lacks the ability to handle the uncertainties which causes the output response of the motor to have excessive overshoot, poor disturbance rejection and poor command-following. This paper presents a particle swarm-optimized model reference adaptive PID controller (PSO MRAC-PID) for precise DC motor speed control. The proposed controller can set the speed of a DC motor to track a predefined reference model and adapt to the effect of uncertainties and external disturbances, using a combination of two basic techniques namely, the Massachusetts Institute of Technology (MIT) rule and particle swarm optimization (PSO). The MIT rule was utilised for the design of the adaptation mechanism and PSO was implemented for optimization of the adaptation gains of the MRAC-PID controller to enhance its overall performance. Comparative analyses with fixed gain PSO-PID and conventional MRAC-PID controllers indicate that the proposed PSO MRAC-PID achieves a 66.7% improvement in settling time, 0.4% improvement in overshoot, 35.3% reduction in control effort and a general improvement in command tracking and disturbance rejection. This outcome suggests that the proposed POS MRAC-PID controller can better suit realworld applications where precision is critical such as motor drives in conveyor belts, crane systems, among others. It also achieves control with greater energy savings as compared to the traditional methods – a crucial feature for greener and cleaner operations.