Modelling the relationship between drinking water turbidity and other indicators of water quality in Al-Sin drinking water purification plant using Dynamic Artificial neural networks could help in the implementation of the stabilization for the performance of the plant because these neural networks provide efficient tool to deal with the complex, dynamic and non-linear nature of purification processes. They have the ability to response to various instant changes in parameters influencing water purification. In this research, four models of feed-forward back-propagation dynamic neural network were designed to predict the effluent turbidity from Al-Sin drinking water purification plant. The models were built based on turbidity, pH and conductivity of raw water data while the effluent turbidity data were used for verify the performance accuracy of each network. The results of this research confirm the ability of dynamic neural networks in modeling and simulating the non-linearity behavior of water turbidity as well as to predict its values. They can be used in Al-Sin drinking water purification plant in order to achieve the stabilization of its performance.