The use of artificial intelligence (AI), machine learning (ML), and real-time data capture to digitally alter biorefineries has drawn a lot of interest recently. The artificial intelligence (AI) and machine learning (ML) technologies analyze operational dynamics, process control to boost productivity, and high-dimensional output data. The focus of this chapter is on the categorization, past and present state of the biorefinery, and biorefineries that have achieved commercial success. There has been discussion on other uses of ML models and algorithms in supply chain analysis, biomass characterization, pretreatment, and physicochemical conversion processes. The strengths and limitations of AI and ML in biorefinery systems are comprehensively analyzed. This review finds that we can decrease the amount of time spent on experiments and increase productivity with the use of artificial intelligence and machine learning. When compared to biological processes, the use of AI and ML-based conversion models in biorefineries is simple, straightforward, and economically viable. In order to have a better knowledge of the biorefinery field’s future, this chapter also presents the difficulties encountered in building and implementing such models.