In the present study, the properties of ternary hybrid nanofluid (THNF) of oil (5W30) - Graphene Oxide (GO)-Silica Aerogel (SA)-multi-walled carbon nanotubes (MWCNTs) in volume fractions (\documentclass[12pt]{minimal}
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				\begin{document}$$\varphi )$$\end{document}φ)of 0.3%, 0.6%, 0.9%, 1.2%, and 1.5% and at temperatures 5 to 65 °C has been measured. This THNF is made in a two-step method and a viscometer device made in USA is used for viscosity measurements. The wear test was performed via a pin-on-disk tool according to the ASTM G99 standard. The outcomes show that the viscosity increases with the increase in the\documentclass[12pt]{minimal}
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				\begin{document}$$\varphi$$\end{document}φ, and the reduction in temperature. By enhancing the temperature by 60 °C, at\documentclass[12pt]{minimal}
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				\begin{document}$$\varphi$$\end{document}φ= 1.2% and a shear rate (SR) of 50 rpm, a viscosity reduction of approximately 92% has been observed. Also, the results showed that with the rise in SR, the shear stress increased and the viscosity decreased. The estimated values of THNF viscosity at various SRs and temperatures show that its behavior is non-Newtonian. The efficacy of adding nanopowders (NPs) on the stability of the friction and wear behavior of the base oil has been studied. The findings of the test display that the wear rate and friction coefficient increased about 68% and 4.5% for\documentclass[12pt]{minimal}
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				\begin{document}$$\varphi$$\end{document}φ= 1.5% compared to\documentclass[12pt]{minimal}
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				\begin{document}$$\varphi$$\end{document}φ= 0. Neural network (NN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Gaussian process regression (GPR) based on machine learning (ML) have been used to model viscosity. Each model predicted the viscosity of the THNF well, and Rsquare > 0.99.