Coupling ML algorithms with MD simulations may provide valuable and robust tools for the effective tuning of PNCs machining processes. In present study, we developed a ML-based QSPR model by integrating the cheminformatics approach and NEMD simulations to predict the viscosity of the PNCs. First, we investigated the rheological property of PNCs using NEMD simulation. We calculated the viscosity of PNCs under different shear rate, NPs loading and temperature conditions. Second, we used the calculated data to train and test four different ML-based QSPR models to predict the viscosity of the PNCs system. Third, the model with the best performance was selected to compare with some physics-based advanced models to verify its reliability. We attempt to derive a universal to predict the viscosity of PNCs with different  ,  and T from ML algorithms.