Prediction of water quality which can ensure the water supply and prevent water pollution is essential for a successful water transfer project. In recent years, with the development of artificial intelligence, the backpropagation (BP) neural network has been increasingly applied for the prediction and forecasting field. However, the BP neural network frame cannot satisfy the demand of higher accuracy. In this study, we extracted monitoring data from the water transfer channel of both the water resource and the intake area as training samples and selected some distinct indices as input factors to establish a BP neural network whose connection weight values between network layers and the threshold of each layer had already been optimized by an improved artificial bee colony (IABC) algorithm. Compared with the traditional BP and ABC-BP neural network model, it was shown that the IABC-BP neural network has a greater ability for forecasting and could achieve much better accuracy, nearly 25% more precise than the BP neural network. The new model is particularly practical for the water quality prediction of a water diversion project and could be readily applied in this field.