This review discusses emerging methods for genome-scale metabolic modeling (GEMs) of microbial communities. GEMs are becoming essential tools for studying mixed microbial populations by integrating biological data with mathematical rigor. However, challenges remain due to the diversity of computational tools, lack of universal standards, and model limitations. The article provides a comprehensive overview of foundational concepts, compares tools based on requirements, capabilities, and applications, and highlights current challenges and future directions in model development. GEMs rely on large-scale mathematical formalization of genomic, physiological, and biochemical data to simulate microbial metabolism at a systems level. Recent advances have enabled automation in GEM reconstruction, driven by new technologies for microbial isolation and metagenome sequencing. This has increased the number of GEMs, emphasizing the need for high-quality models. Reconstruction tools must be evaluated to guide users in selecting appropriate methods for different scenarios, especially for multispecies models. The review outlines key steps in GEM reconstruction, including genome annotation, metabolic network modeling, and gap-filling. It discusses various approaches for testing and refining GEMs, such as flux balance analysis (FBA), which is used to simulate microbial communities and elucidate metabolic interactions. The article also covers different types of model reconstruction methods, including automated and semi-automated approaches, and highlights the importance of quality control and biological constraints in ensuring model accuracy. Community metabolic modeling under steady-state or dynamic assumptions is discussed, along with compartmentalization strategies that define how microbial species interact. The review also addresses the integration of multi-omics data and the use of machine learning to enhance GEMs, improve predictive performance, and facilitate interpretation. Finally, the article outlines outstanding questions and future directions, including the need for standardized models, improved validation techniques, and better integration of context-specific data. Overall, the review emphasizes the growing importance of GEMs in understanding microbial communities and their applications in biotechnology and medicine.This review discusses emerging methods for genome-scale metabolic modeling (GEMs) of microbial communities. GEMs are becoming essential tools for studying mixed microbial populations by integrating biological data with mathematical rigor. However, challenges remain due to the diversity of computational tools, lack of universal standards, and model limitations. The article provides a comprehensive overview of foundational concepts, compares tools based on requirements, capabilities, and applications, and highlights current challenges and future directions in model development. GEMs rely on large-scale mathematical formalization of genomic, physiological, and biochemical data to simulate microbial metabolism at a systems level. Recent advances have enabled automation in GEM reconstruction, driven by new technologies for microbial isolation and metagenome sequencing. This has increased the number of GEMs, emphasizing the need for high-quality models. Reconstruction tools must be evaluated to guide users in selecting appropriate methods for different scenarios, especially for multispecies models. The review outlines key steps in GEM reconstruction, including genome annotation, metabolic network modeling, and gap-filling. It discusses various approaches for testing and refining GEMs, such as flux balance analysis (FBA), which is used to simulate microbial communities and elucidate metabolic interactions. The article also covers different types of model reconstruction methods, including automated and semi-automated approaches, and highlights the importance of quality control and biological constraints in ensuring model accuracy. Community metabolic modeling under steady-state or dynamic assumptions is discussed, along with compartmentalization strategies that define how microbial species interact. The review also addresses the integration of multi-omics data and the use of machine learning to enhance GEMs, improve predictive performance, and facilitate interpretation. Finally, the article outlines outstanding questions and future directions, including the need for standardized models, improved validation techniques, and better integration of context-specific data. Overall, the review emphasizes the growing importance of GEMs in understanding microbial communities and their applications in biotechnology and medicine.