Recently, artificial neural networks (ANNs) have been used successfully for many engineering problems. This paper presents a practical way of predicting the hydropower energy potential using ANNs for the feasibility of adding a hydropower plant unit to an existing irrigation dam. Because the cost of energy has risen considerably in recent decades, addition of a suitable capacity hydropower plant (HPP) to the end of the pressure conduit of an existing irrigation dam may become economically feasible. First, a computer program to realistically calculate all local, frictional, and total head losses (THL) throughout any pressure conduit in detail is coded, whose end-product enables determination of the C coefficient of the highly significant model for total losses as: THL = C.Q(2). Next, a computer program to determine the hydroelectric energies produced at monthly periods, the present worth (PW) of their monetary gains, and the annual average energy by a HPP is coded, which utilizes this simple but precise model for quantification of total energy losses from the inlet to the turbine. Inflows series, irrigation water requirements, evaporation rates, turbine running time ratios, and the C coefficient are the input data of this program. This model is applied to randomly chosen 10 irrigation dams in Turkey, and the selected input variables are gross head and reservoir capacity of the dams, recorded monthly inflows and irrigation releases for the prediction of hydropower energy. A single hidden-layered feed forward neural network using Levenberg-Marquardt algorithm is developed with a detailed analysis of model design of those factors affecting successful implementation of the model, which provides for a realistic prediction of the annual average hydroelectric energy from an irrigation dam in a quick-cut manner without the excessive operation studies needed conventionally. Estimation of the average annual energy with the help of this model should be useful for reconnaissance studies.