Automatic generation control of a two-area power network in an interconnection system is considered. The most effective controller is determined by comparing the performance of three controllers: Proportional-Integral (PI), Proportional-Integral-Derivative (PID), and Artificial Neural Network (ANN) controllers. For the analysis and evaluation of system frequency and tie-line power deviations, MATLAB/Simulink is utilized. Rise time, settling time, overshoot, undershoot, and slew rate are evaluated and compared for both frequency deviation and tie-line power responses. The comprehensive simulation results, supported by tables and bar graphs, indicate that the ANN controller outperforms the classical controllers in terms of faster response, enhanced damping of system oscillations, reduced settling time, enhanced accuracy, and efficient power transfer. The bar graphs further emphasize the superiority of the ANN controller in terms of frequency deviation and tie-line power response parameters by providing visual representations of the data. This study contributes to the comprehension and advancement of AGC control strategies in interconnected power networks by highlighting the potential of ANN controllers to achieve greater stability and faster, more precise regulation. For further improvement, future research directions include optimizing the ANN controller's hyperparameters and investigating advanced neural network approaches such as ReLU activation functions. The combination of quantitative analysis and visual representations provides a comprehensive evaluation of controller performance in AGC applications, thereby facilitating well-informed power system control decisions.