: Frequency stabilization in interconnected power systems has become increasingly challenging due to the integration of hybrid generation sources and varying load conditions. Conventional Load Frequency Control (LFC) methods, such as ANN-based coordinated control, utilize energy storage systems and thermal power units to enhance system performance. These approaches employ techniques like Wavelet Packet Decomposition for control signal generation and backpropagation neural networks for disturbance identification. Although effective, such methods suffer from high computational complexity and dependency on training data, limiting their real-time applicability. To address these challenges, this paper proposes a Teaching-Learning-Based Optimization (TLBO) tuned Proportional-Integral-Derivative (PID) controller for Automatic Generation Control (AGC). The proposed controller optimally adjusts its parameters using the TLBO algorithm, which requires fewer tuning parameters and offers faster convergence compared to other metaheuristic techniques. The optimization objective focuses on minimizing frequency deviations and tie-line power fluctuations across interconnected areas. The proposed TLBO-based PID controller is implemented in a multi-area power system incorporating diverse generation sources, including thermal and renewable units. Simulation results demonstrate that the proposed method significantly improves frequency stability, reduces settling time, and enhances overall system performance under varying operating conditions. Hence, the TLBO-optimized PID controller provides a robust and efficient solution for modern power system frequency regulation.