Recent studies on photo voltaic system (PVs) have concentrated on minimize the costs and maximize the conversion effectiveness. In order to maximize the effectiveness of PV energy conversion systems, solar panels should be operated at maximum power points (MPP). At MPP, solar arrays generate the electric energy at maximum efficiency and minimum losses. Solar cells have variable current and voltage characteristics and MPP depends on solar irradiations and ambient temperature. So a maximum power tracking control should be made rapidly in different temperature and solar radiation conditions. In this study; solar cell was obtained by using equivalent circuit of solar cell with Matlab / Simulink connected with dc-dc converter to fed brushless dc motor BLDC and water pumping system, this paper drive using adaptive neuro-fuzzy inference system (ANFIS) technique. The proposed (ANFIS) controller is trained off-line to identify the optimum output power of P-V system and desired water flow rate. This paper describes the operation of the system and the development of component models for the array, using ANFIS technique to enhance performance of system and to get the maximum power from P-V under different conditions of the solar insolation and temperature to get maximum flow rate of water to water pumping system WPS. An iteration technique which is mathematical–based is also presented for comparison and evaluation purposes. The paper indicates accuracy, robustness and effectiveness of the proposed models in water flow rate control, economic feasibility and fault detection.