This research suggests new experimental outcomes regarding the viscosity and thermal conductivity of silver, copper and titanium oxide nanoparticles dispersed in mineral insulating oil by high-pressure homogenization process without using any additives or surfactants. Later, via employing non-linear regression, an adaptive neuro-fuzzy inference system (ANFIS) and achieved experimental data, new models were evolved to predict the viscosity besides thermal conductivity of nanofluids. For modelling, viscosity as well as thermal conductivity of nanofluids was picked as the target factor, and the volume concentration in addition to types of nanoparticles was regarded as the design (input) factors and all experimental datawas classified into a train and a test data set. The model was conducted through the train set and the outcomes were contrasted with the experimental data set. Predicted thermal conductivities as well as viscosities were compared with experimental data for three dif- ferent nanofluids, having nanoparticles volume concentrations of 0.00125% and 0.050%. A comparisonwasmade between the ANFIS and regression outcomes. To evaluate the results, the coefficient of determination (R2 )and root-mean-square error (RMSE) are reported. The achieved results of this research indicate that thermal conduc- tivity of nanofluids enhance by nanoparticles concentration increment. Thermal conductivity of silver is higher compared to thermal conductivity of titanium oxide and copper nanoparticles. According to the ANFIS and non-linear regression outputs, two sets of correlations for calculating the dynamic viscosity as well as thermal conductivity were suggested. Comparing the experimental data with suggested correlations demonstrate very good agreement between the suggested correlations and experimental data. However, equations of previous researches would not be perfectly able to predict the experimental data of present study.

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