Bioenergy is utilized as a sustainable substitute of the fossil energies. It is a fact that applications of bioenergy products on a large scale are limited because of various challenges of the complexity and nonlinearity of the biofuel production process, feedstock variability, handling, optimization of conversion economics, and supply chain reliability. These challenges can be overcome using rapid and accurate data-based technologies of artificial intelligence (AI) that aid in design optimization, monitoring, and future forecasting. Artificial intelligence systems comprise the areas of symbolic AI (fuzzy logic, case-based reasoning), machine learning approaches (interconnected nodes utilization of artificial neural network (ANN) function, support vector machine (SVM), Bayesian network, random forest), heuristics (optimization through algorithm of particle swarm, genetic algorithm), and hybrid systems (adaptive neuro-fuzzy interference system, agent-based modeling). This chapter provides an understanding of how artificial intelligence systems operate and how they are set up to be used in the production life cycle of biofuel. It also sheds light on the previous research based on machine learning techniques in predicting biomass properties, biomass conversion performance, bioenergy consumption systems, supply chain optimization, and modeling beginning with the collection of feedstock to end use, engine performance, and emission phase. Thus, AI and machine learning has a function in overcoming the challenges of bioenergy production and improving yield and quality.