Laser machining is a typical non-conventional subtractive type manufacturing process. This process uses thermos-energy due to impingement of photons for removing material from surface of metals as well as non-metals through heating, melting, and vaporizing. Lasers can be used for welding, cladding, marking, surface treatment, drilling, and cutting along with other manufacturing processes. Al 7075 has good fatigue strength and can be comparable with steels with average machinability. Al 7075 are used in marine, automotive, shipbuilding, electronics, medical industries, and aviation application due to high specific strength. Artificial neural network can be used to predict responses without performing the experiments at all combination of input parameters based on few experiments through design of experiments. For this purpose, central composite design of Response Surface Methodology is utilized. This research paper aims to study the kerf qualities, i.e., kerf depth, kerf width, and heat affected zone produced in Al 7075 alloy as a result of Diode Pumped Fiber Laser Machining and development of artificial neural network model to predict responses. Feed-forward back-propagation ANN with 2 hidden layers and 7 neurons per layer showed the best result for the prediction of responses. Input parameters selected are: laser beam power, pulse frequency, and scanning speed.