In view of the climate change concerns, there is a growing shift toward the advancement of green and renewable energy solutions to ensure a sustainable future. Renewable energy sources hold the potential to substantially supplant non-renewable fossil fuels, thereby promoting sustainable growth, advancing energy innovation, and mitigating reliance on conventional energy systems. However, the conventional techniques utilized in biorefineries to analyze and optimize biofuel production systems are frequently complex, time-intensive, and laborious. This highlights the pressing need for the development of more efficient and streamlined approaches to enhance the feasibility and scalability of biofuel technologies. The modern artificial intelligence (AI) holds immense potential for addressing high-dimensional, and complex scientific challenges while enhancing decision-making processes within renewable energy sectors. AI can revolutionize the biofuel industry by addressing critical challenges in the production of biofuels from lignocellulosic biomass, including feedstock optimization, supply chain management, and process optimization. By AI-driven approaches, such as crop yield prediction, waste biomass utilization, and genetic engineering, the chapter demonstrates how AI can maximize the potential of lignocellulosic feedstocks. Furthermore, AI's transformative role is explored in biofuel production processes, including fermentation, hydrolysis, and transesterification, showcasing significant improvements in efficiency, cost reduction, and product quality. Through predictive modeling and in silico testing, AI accelerates the discovery of novel methods for producing biofuels from lignocellulosic biomass, underscoring its pivotal role in advancing scalable and sustainable energy solutions. This chapter establishes the critical linkage between biofuel production from lignocellulosic biomass and artificial intelligence, illustrating the transformative capabilities in overcoming technical and operational barriers.