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Providing a robust and reliable model is essential for hydro-environmental and public health engineering perspectives, including water treatment plants (WTPs). The current research develops an emerging evolutionary data-intelligence model: extreme learning machine (ELM) integrated with kernel principal component analysis (KPCA) to predict the performance of the Tamburawa WTP in Kano, Nigeria. A traditional feed-forward neural network (FFNN) and a classical linear autoregressive (AR) models were also employed to compare the predictive performance. For this purpose, different input data with the corresponding treated pH, turbidity, total dissolve solids, and hardness as the target variables obtained from the WTP were used. The predictive models are evaluated based on the three numerical indices, namely Nash-Sutcliffe (NC), root mean squared error (RMSE) and mean absolute percentage error (MAPE …