Polymer solutions are designed to develop a favorable mobility ratio between the injected polymer solution and the oil water bank being displaced by the polymer. Subsequently, a more uniform volumetric sweep of the reservoir is produced. Chemical and mechanical degradation of the polymer solutions, on the other hand, reduce their viscosity which significantly affects their performance. The primary objective of this study is to investigate the effect of surface modification of silica nanoparticles (NPs) on the effective viscosity of partially hydrolyzed polyacrylamide (HPAM) and xanthan gum (XG) solutions at different NP concentrations and temperatures. The chemical functionalization of SiO2 NPs with carboxylic acids and silanes was confirmed by FTIR measurements. The experimental results showed that the addition of SiO2 NPs increased the viscosity of XG solutions due to the formation of three-dimensional structures between the silica NPs and the polymeric chains. The thickening effect of HPAM was improved by the addition of silica NPs modified with 3-(methacryloyloxy)propyl] trimethoxysilane (MPS), octyl triethoxysilane (OTES), and oleic acid-method A (OAA). In addition, the HPAM and XG nanopolymer sols of modified silica NPs showed more temperature and brine tolerance than that of unmodified silica NPs. A model was developed based on multilayer perceptron (MLP) neural network for predicting viscosity of nanopolymer sols using 9900 data points. The MLP model was trained by Bayesian Regularization (BR), Levenberg-Marquardt (LM), Resilient Backpropagation (RB), and Scaled Conjugate Gradient (SCG) algorithms. The results revealed that the BR-MLP model outperformed the three other models and could predict all the viscosity data with an average absolute relative error of 2.46% and R-2 of 0.999.