This paper studies the modeling designs and experimental analysis of artificial intelligence fuzzy logic-based automatic generation control (AGC) for three-area interconnected power systems. Due to varying structures, random load fluctuations, nonlinearities, constraint vagueness and sophistication of a power system that cause change in frequency and tie line power. Equipment malfunctions, potentially even collapses and cascading occurrences, will result from system instability. Soft computing-based intelligent controllers are required for real-time operation in order to handle this. FL-PID can provide a high level of adaptation to changing conditions, have the ability to make a decision quickly by processing imprecise information, and perform effectively even with nonlinearities. The integrated time square error (ITSE) and integrated time absolute error (ITAE) are the objective function for building the controller. Also, the modeling and design of the control were done in Matlab/Simulink to evaluate the dynamic performance index improvements of the controllers in terms of steady-state error, rise time, settling time, and overshoot. The test findings in this research demonstrate how well the suggested controller works with erratic step load variations. The outcomes display that FL-PID was used to improve performance and intelligently control the selection of parameters to achieve effective power control compared to FL only and FL-PI control of the AGC.