Sustainable polymer composites have attracted significant interest in recent years, not only due to its technological applications, but also because it combines ‘sustainability’, one among the important goals of research and technology (Inamuddin et al. in Sustainable polymer composites and nanocomposites. Springer, 2019). The properties of such composites are greatly influenced by the components incorporated within the system. Designing composites for specific applications thus requires careful selection of the components and specific understanding of the interactions influencing the characteristics of such systems; which can be deciphered using theoretical calculations (Shen et al. in Chem Rev 123:2242−2275, 2023). This chapter thus introduces briefly the computational methodology used in predicting the properties of such composite materials. Molecular dynamics simulations, one such method which studies the properties of bulk materials by allowing it to move for a specific time, have played a significant role in this direction in determining the mechanical and thermal properties of a series of polymeric systems (Zhou et al. in Adv Func Mater 32(14):2109881, 2022). First principal calculations, involving density functional theory, mark another notable method, involving quantum calculations for determining atomistic properties influencing the composite behaviour (Chen et al. in Comput Mater Sci 188:110229, 2021). Simulations including molecular dynamics, Monte Carlo and coarse grain model, etc. are also discussed in brief with examples (Wang and Gómez-Bombarelli in npj Comp Mater 5:1–9, 2019; Zhang et al. in Int J Geomech 21:04021048, 2021). Apart from this, the chapter also highlights the possibilists of utilizing computational approaches employed for other molecules including machine learning (artificial intelligence), for predicting the properties of sustainable polymeric materials (Han et al. in Constr Build Mater 244, 2020).