DC machine usage in Electric Vehicles (EVs) has been gaining a notable amount of focus. The speed of DC motor-driven wheels in an EV changes as it encounters disturbances like a reduction in tire air volume, or a corrugated and rugged surface on which it is driven. This continuous disturbance and variation in speed could result in the exertion of EV circuits, which can be fatal for passengers. Hence, a control method that could respond to the disturbance and give a signal to the motors of the wheels for automatic speed control is required. Thus, this paper proposes artificial neural network (ANN)-based control strategies for enhanced speed regulation in DC motor-driven electric vehicles. This paper highlights the focus on ANN control strategies in the context of DC motors of EVs. Different ANN architectures such as radial bias network (RBNN), probabilistic neural network (PNN), feed-forward network (FFNN), Elman network, NARX network, NAR network, and recurrent neural network (RNN) are implemented to design the gains of the PID (Proportional–Integral–Derivative) control loop of the DC motor. A thorough analysis concerning different activation functions, mean squared error, mean absolute error, and weight-bias functions is provided. The efficacy of all these methods is tested when the EV system is subjected to key disturbances, namely, step, ramp, sinusoidal, and chirp. System responses under all these test conditions for all the ANN architectures are drawn. A better ANN architecture to tune the PID controller is recommended based on these transient characteristics and disturbance rejection ability. From the results, it is observed that the performance of FFNN is superior to that of other ANNs due to its shorter rise time, less peak overshoot, lower delay time, and lower steady-state error. Thus the proposed work leverages the usefulness of ANNs to achieve more precise speed control, enhancing the overall performance of EVs.