Rising electricity demand causes mismatches between generation and demand, leading to frequency deviations from the nominal 50 Hz. Ethiopia's hydropower plants rely on primary load frequency control mechanisms; however, a supplementary mechanism is needed to maintain frequency centrally. This paper presents a hybrid neuro-fuzzy inference system-based fuzzy-proportional-integral-derivative controller and compares it with a fuzzy-proportional-integral-derivative controller, a neuro-fuzzy inference system-based controller, and a conventional proportional-integral-derivative controller within a three-area load frequency control model. The aim is to provide a robust, supplementary, centralized mechanism for the Ethiopian Electric Power grid that resolves challenges in real-time controller-parameter tuning. The hydro-governor, hydraulic turbine, synchronous generator, tie-line, and load were modeled, and a three-area system model was formulated. All four controllers were designed in MATLAB/Simulink using three governor models, and their performance was evaluated comparatively. Frequency and tie-line power flow changes, which determine the area control error, serve as feedback to the governor for regulating turbine water flow. Response was assessed via overshoot, undershoot, steady-state error, and settling time. The proposed hybrid controller significantly outperforms the alternatives: in Scenario 1, settling time fell to 12.959 seconds with an integral time absolute error of 0.00565, versus 48.8824 seconds and 0.0838 for the conventional controller. Under multi-area disturbances, the hybrid controller retained the lowest error of 0.02375, and robustness analysis under plus/minus twenty-five percent parameter variations confirmed stability. The novelty lies in hybridizing a Mamdani fuzzy-gain scheduler with a Sugeno-type neuro-fuzzy system trained on multi-governor transient data, a combination not previously reported.