The bio-oil derived from the biomass fast pyrolysis, after being upgraded on a suitable catalyst, can replace existing fossil fuel. However, catalytic upgrading of produced bio-oil is a complex process because of its multi-component nature, which results in a complicated, multi-step reaction network under the catalytic environment. Numerous theoretical researches have been performed to provide insights into catalytic upgrading of bio-oil, which cannot be elucidated using experiments. The chapter describes the newer theoretical approaches in the catalytic upgrading of bio-oil. The theoretical modeling tool, such as density functional theory (DFT) revealed crucial insights of bio-oil upgrading over a catalyst in terms of multiple reactions, kinetics, and energetics. The hydrodeoxygenation of bio-oil compounds using a catalyst is being appeared as one of the most promising routes to obtain oxygen-deficient and hydrocarbon-rich bio-oil, suitable to be matched with fossil fuel. The employment of machine learning approaches to facilitate and improve the biomass fast pyrolysis with particular emphasis on bio-oil is also addressed and briefly introduced in this chapter.