DC motor speed control is fundamental in modern industrial and robotic systems, where high precision, robustness, and energy efficiency are required. Conventional integer-order PID controllers often fail to capture the nonlinearities and parameter variations inherent in real DC motors. This paper proposes a control framework combining fractional-order system identification with an optimized Fractional-Order PID (FOPID) controller. The five FOPID parameters are optimized using four metaheuristic algorithms: PSO, GA, ABC, and ACO. Experimental validation on a MATLAB/Simulink–ARDUINO/DC motor platform demonstrates that the PSO-optimized FOPID controller achieves a settling time of 1.08 seconds with 2.00% overshoot and a control effort of 1.8 V·√s. Compared to the extended Ziegler-Nichols tuned FOPID, the PSO approach achieves 98.57% faster settling while maintaining comparable overshoot and demonstrating superior energy efficiency. Among the metaheuristic algorithms tested, PSO demonstrates the best overall performance with the lowest identification error and the most energy-efficient control effort. These results confirm the superiority of the metaheuristic optimization approach over conventional tuning methods in terms of dynamic response, precision, and robustness for fractional-order control systems.