The widespread use of direct current (DC) motors has persisted despite advancements in power electronics devices. These motors find application not only in industrial drives and solar-powered electric vehicles but also in everyday household devices. This work delves into the speed control of Separately Excited DC Motors, exploring the efficacy of classical Proportional Integral (PI) Controllers alongside advanced soft computing Intelligent Controllers, namely Fuzzy Logic Controllers (FLC), Adaptive Neuro Fuzzy Inference System (ANFIS) Based Controllers, and Artificial Neural Network (ANN) Based Controllers. The investigation is carried out using MATLAB and the Simulink environment. DC motors convert electrical energy into mechanical work, facilitating a variety of tasks. They are classified based on the excitation of field windings: Self Excited DC Motors derive their field coil power from the same DC source as the armature coils, while Separately Excited DC Motors receive field power from a distinct source. Speed control is crucial for achieving desired operational levels in various applications. Two primary methods are employed: armature voltage control and field current control. In this study, the Armature voltage control technique is employed for speed control, comparing the performance of PI controllers with soft computing approaches. The study builds on existing research, drawing from literature such as the use of Fuzzy Logic Controllers to manage DC motor operations