Polymeric materials provide significantly enhanced mechanical, electrical, and thermal characteristics in comparison to their larger-scale equivalents. In addition, nanocomposites present distinctive design possibilities, offering substantial benefits in creating multifunctional materials customized for specific uses. The emergence of artificial intelligenceArtificial Intelligence (AI) (AI) and machine learningMachine Learning (ML) (ML) has transformed predictive modelling, allowing for unprecedented investigation of system properties that were previously inaccessible using traditional computational and experimental methods. The purpose of this chapter is to give a succinct summary of the significant developments in using machine learningMachine Learning (ML) to predicting properties of polymeric materials. This chapter explores different machine learningMachine Learning (ML) methods employed in the analysis of polymeric nanocomposites to estimate material characteristics. The study specifically examines the utilization of Support Vector RegressionSupport Vector Regression (SVR) (SVR), Decision Tree RegressionDecision Tree Regression (DTR) (DTR), Linear RegressionLinear Regression (LR) (LR), and Gaussian Process RegressionGaussian Process Regression (GPR) (GPR) models to forecast log(viscosity) in centipoise (cP).