Abstract: The CuO-ZnO/(ethylene glycol(EG)-water) nanofluid with CuO-ZnO mass fractions of 0–3 wt% were prepared by two-step method. First, the mixing mass ratio of nanoparticles CuO to ZnO was fixed at 50∶50, and the mass ratios of EG to deionized water varied from 20∶80 to 80∶20. Variations of thermal conductivity with temperature (25–60℃) and mixing mass ratios of base liquids were studied. Second, the radial basis function neural network (RBFNN) model was used to predict the thermal conductivity. In the model, the mass fraction, temperature and mixing mass ratios of base liquids were considered as independent variables while the thermal conductivity is the dependent variable. The predicted values were compared with the values predicted by back propagation neural network (BPNN) and multiple linear regression (MLR). The results show that the thermal conductivities of CuO-ZnO/(EG-water) nanofluid increase nonlinearly with the increase of temperature, while decrease with the increase of mixing mass ratios of base liquids. Compared with the thermal conductivity of the base fluid, it increases from 14.03% to 23.47% at the mass fraction of 3 wt% and the mixing mass ratio of base fluid of 20∶80. The thermal conductity of CuO-ZnO/(EG-water) changes nonlinear with random motion of nanoparticles and temperature. The results obtained by RBF model are more precise than that predited by BPNN model and MLR model. The model evaluated indexes of root mean square error (RMSE), mean relative percentage error (MRPE) and sum of squared error (SSE) are closer to 0 and duostatistical coefficient of multiple determination R² is closer to 1 indicating that the RBFNN model can accurately predict the thermal conductivity. It can also be used to characterize the effect of various parameters on the thermal conductivity. The results offer an effective method to establish the data-driven model to accurately predict the thermal conductivity of CuO-ZnO/(EG-water) nanofluid.