In modern society, the use of computational modeling has increased significantly in a variety of industries. This increase is especially noticeable because of growing concerns about the rapid depletion of fossil fuels and their harmful effects on the environment. As a result, there has been a lot of interest in researching alternative energy sources like biofuels. It becomes clear that computational modeling is a useful and affordable tool that can maximize output and financial sustainability in the processes involved in the production of biofuels. Notably, developments in artificial intelligence and related fields have made it possible to predict a number of parameters that are essential to the production of biofuels, such as substrate compositions, process yields, and ideal operational conditions for sugar fermentations. It can be used in a wide range of bioprocesses, including the pretreatment of lignocellulosic biomass and different phases of biofuel synthesis. The incorporation of computational modeling exhibits potential to mitigate the dependence on preliminary experiments and, in certain cases, eliminate the necessity for lab-scale trials, consequently optimizing the process of research and development. This chapter examines potential trends of biofuels and bioenergy and emphasizes the importance of computational modeling for maximizing biofuel yields while providing extensive explorations of computational and in silico strategies employed for biofuel productions, emphasizing their pivotal role in advancing sustainable bioenergy production.