In conventional control techniques based on power system linearization around a given point, outputs are dissatisfactory under sudden variations in load and displacement of the working point. Therefore, several methods have been introduced to adapt such controllers to variations in system operations. This paper proposes an intelligent load frequency control (LFC) technique for multi-area power systems with an emotional neural network (ENN), the parameters of which are tuned through the bird swarm algorithm (BSA). The proposed emotional learning-based intelligent controller is inspired by the mammalian brain limbic system in light of its effectiveness and efficiency in control applications. Simulations were performed in MATLAB on an intrinsically nonlinear three-area power system under sudden load and parameter changes. The proposed model was compared to classical proportional–integral–derivative (PID), standard ENN, and indirect adaptive fuzzy controllers under various operating conditions and parameter changes. According to the results, the proposed methodology was faster and more accurate in controlling frequency variations and power transfer changes between power system areas. The main novelty of this work lies in the integration of a continuous radial basis emotional neural network (CRBENN) with a Lyapunov-based direct adaptive robust control framework (DARENC), optimized via Bird Swarm Algorithm (BSA), for load frequency control in multi-area power systems under uncertainties.The key novelty of this work lies in the integration of a continuous radial basis emotional neural network (CRBENN) with a Lyapunov-based direct adaptive robust control framework (DARENC), optimized via Bird Swarm Algorithm (BSA), for load frequency control in multi-area power systems under uncertainties.