Both air flow mechanics and control system of a wind turbine play an important role in its energy management system. Energy is considered as the essential part for the national development in the form of mechanical power or any other which has major contribution for improving the quality of life and enhancing the economic growth. Subsequently, power generation by wind velocity is a complex process with many interacting factors such as wind velocity, climate condition, natural disaster, control system, design structure, vane tip speed ratio, centrifugal force, rotor drag, turbulence flow, roughness and wind shear, etc. Various procedures have been reported in literature to achieve an optimal performance and system effectiveness. However, these control methods had depended only on exact mathematical modelling or on expert's knowledge that can’t be relied on solely in modelling such a complex management of air flow mechanics due to its unstable climate condition and so on. In the proposed study, an integrated control model using an adaptive neuro- fuzzy inference system for wind turbine power management strategy has been introduced. In this model, an artificial neural network is employed to develop the fuzzy expert system in order to achieve a more realistic evaluation of wind power extraction. Simulations and experiments have been carried out to investigate the effect of control strategy parameters of the wind turbine and its power extraction.