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Abstract Photovoltaic/thermal (PV/T) are high-tech devices to transform solar radiation into electrical and thermal energies. Nano-coolants are recently considered to enhance the efficiency of PV/T systems. There is no accurate model to predict/optimize the PV/T systems’ electrical efficiency cooled by nano-coolants. Therefore, this research employs machine-learning approaches to simulate PV/T system electrical performance cooled by water-based nanofluids. The best topology of artificial neural networks, least-squares support vector regression, and adaptive neuro-fuzzy inference systems (ANFIS) are found by trial-and-error and statistical analyses. The ANFIS is found as the best method for simulation of the electrical performance of the considered solar system. This approach predicted 200 experimental datasets with the absolute average relative deviation (AARD) of 13.6%, mean squared error (MSE) of 2.548 …