Sustainable process engineering paradigm is changing with the development of Artificial Intelligence (AI) combinable with biorefinery reactor simulation. This chapter describes the ongoing convergence of AI technology including machine learning, deep learning, reinforcement learning, and evolutionary algorithms, coupled with conventional process simulation scripts to transform the reactor design in biorefineries systems. Highlighting the shortcomings of the traditional approaches that use empirical models and trial and error to optimize reactor design, the chapter introduces AI-based solutions that can work out complex design spaces and recommend new and novel reactor configurations and combinations that would enhance performance and sustainability characteristics. AI methods can be used both to optimize more traditional types of reactors, such as CSTRs, PFRs, and packed beds, and to help develop new kinds of reactor concept, in which reaction and separation processes are combined. The potential of the AI-based design is observed in case studies featuring various solutions to the improvement of lignocellulosic biomass conversion, algal biorefineries, and waste-to-energy systems. Some of the other implications of AI adoption that are considered in the chapter include the challenge of data quality, model validation, computation needs, and obstacles to adopting in industry. In addition, it envisions the future, like quantum computing, explainable AI, and smart manufacturing to live in the industry 4.0 paradigm.