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Providing a satisfactory and reliable prediction tool for Turbidity in water treatment plant is quite an essential task for various environmental and public health perspective. In this paper, a neuro-fuzzy approach is developed using two different optimizations of fuzzy inference system (FIS) (i.e. hybrid and backpropagation) to predict the treated Turbidity at Tamburawa water treatment plant (TWTP). Subsequently, a neuro-fuzzy ensemble technique was applied to improve the performance of the two optimizations. For this purpose, the daily recorded data of turbidity (Turb _R ) (μs/cm), conductivity (Cond _T ) (mS/cm), total dissolve solid (TDS _R ) (mg/L), chloride (mg/L) and suspended solid (SS _R) (mg/L) and Hardness (Hardness _T ) (mg/L) from TWTP were obtained. The predictive models were evaluated based on two numerical indicators (determination coefficient and root mean square error). The obtained results …