Biorefineries are becoming an important part of the circular bioeconomy since they allow renewable biomass to be converted into fuels, chemicals, energy, and other useful products. At a time when issues such as climate change, fossil fuel shortages, waste generation, and energy security are growing, they offer a promising alternative to conventional fossil-based systems. Different models, like algal biorefineries or those based on agro-food waste, show how biomass can be better utilized to recycle nutrients, support sustainable farming, and build closed-loop production systems. To properly assess the sustainability of these systems, reliable evaluation tools are needed. Techno-economic analysis (TEA) is used to check whether a process is economically feasible, highlight where the main costs lie, and help guide investments. On the other hand, life cycle assessment (LCA) measures the environmental impacts of a process from the start (raw material) to the end (disposal or recycling). More recently, combined approaches such as environmental techno-economic assessment (ETEA) and life cycle sustainability assessment (LCSA) have been suggested, as they also bring in social and broader sustainability aspects. Some case studies, such as reductive catalytic fractionation (RCF), clearly show that using TEA and LCA together can influence both design decisions and policy development. However, combining these methods is not straightforward. Issues such as inconsistent system boundaries, incomplete datasets, and limited inclusion of social factors often create problems. To overcome these, there is a need for standardized methods, improved ways to assign economic value to environmental impacts, and clearer reporting of results. This chapter presents a basic framework for sustainable, cost-effective biorefineries. It highlights the role of stochastic modeling in addressing uncertainty, the use of process design tools to balance cost and sustainability, and the application of machine learning to improve predictive tools like TEA and LCA. In the future, new digital tools such as digital twins, AI-based hybrid models, and open-source simulation platforms may further improve TEA–LCA integration. Taken together, these advances suggest that biorefineries can become not only profitable but also adaptable, environmentally responsible, and socially useful systems that support long-term sustainable development.