The power system continuously experiences various disturbances, which lead to lowfrequency oscillations. It takes place among the rotors of the synchronous generator, which is connected to the power system. Low frequency oscillation (LFO) may sustain and grow in magnitude to cause system interruption if no adequate damping is provided. Power system stabilizer is one of the signals generating control devices to solve this oscillation problem. In this research, the damping of the LFO with an oscillation frequency of 1.1512Hz in the Tis Abay II hydroelectric power plant was presented by designing a mathematically model-free adaptive network fuzzy inference system-based power system stabilizer (ANFIS-based PSS) to improve the transfer capability and small-signal stability. To verify the capability of suggested stabilizer and system stability enhancement comparative analysis of existing system without controller, with conventional power system stabilizer (CPSS) and with proposed controller is performed using the time domain simulation of linearize model of Tis Abay II Hydropower plant in MATLAB SIMULINK software. The time domain simulation results show that the steady state response of the system settling time improved. The CPSS improved the settling time from 9.9377 to 4.5989sec, 9.9777 to 4.48448sec,9.9777 to 5.4164 sec, and 9.9744 to 4.7151sec for rotor speed, power angle, electrical, and accelerating torque parameters respectively. In this approach, the hybrid-learning can tune the fuzzy rules and membership function of the fuzzy logic in the ANFIS system. The dynamic performance of the proposed system was investigated for a given operating conditions for system parameters using eigenvalue and time response analysis. The ANFIS-based PSS improved the settling time from 9.9377 to 3.9807sec, 9.9777 to 4.78220sec,9.97776 to 5.0088sec and 9.9744 to 2.0480sec for rotor speed, power angle and electrical accelerating torque respectively. The neuro fuzzy system combines the characteristics of fuzzy logic systems with their ability to deal with imprecise knowledge, and neural networks, with their advantages of establishing a relationship between the system's inputs and outputs, are represented as qualified tools for systems of unknown plan. Keywords: Adaptive Network Fuzzy Inference System (ANFIS), Low Frequency Oscillation (LFO), and Conventional Power System Stabilizer (CPSS).