The article provides a comprehensive overview of genome-scale metabolic modeling (GEM) for microbial communities, highlighting the principles, methods, and tools used in GEM reconstruction and analysis. It begins by discussing the importance of high-quality genome sequencing and annotations in the context of GEM reconstruction, emphasizing the role of computational tools in automating and improving the process. The article then delves into the different approaches for GEM reconstruction, including automated and semi-automated methods, and the importance of gap filling and quality control. It reviews various computational tools for model reconstruction, editing, and testing, categorizing them based on their functionalities and input requirements. The article also explores the simulation and analysis methods using GEMs, such as steady-state and dynamic approaches, and the integration of omics data to enhance the accuracy and applicability of GEMs. Additionally, it discusses community metabolic modeling under steady-state and dynamic assumptions, focusing on compartmentalization techniques and the challenges and future directions in this field. The article concludes by addressing outstanding questions and future directions, including the need for unified standards, the integration of context-specific metadata, and the application of machine learning techniques to improve GEMs.The article provides a comprehensive overview of genome-scale metabolic modeling (GEM) for microbial communities, highlighting the principles, methods, and tools used in GEM reconstruction and analysis. It begins by discussing the importance of high-quality genome sequencing and annotations in the context of GEM reconstruction, emphasizing the role of computational tools in automating and improving the process. The article then delves into the different approaches for GEM reconstruction, including automated and semi-automated methods, and the importance of gap filling and quality control. It reviews various computational tools for model reconstruction, editing, and testing, categorizing them based on their functionalities and input requirements. The article also explores the simulation and analysis methods using GEMs, such as steady-state and dynamic approaches, and the integration of omics data to enhance the accuracy and applicability of GEMs. Additionally, it discusses community metabolic modeling under steady-state and dynamic assumptions, focusing on compartmentalization techniques and the challenges and future directions in this field. The article concludes by addressing outstanding questions and future directions, including the need for unified standards, the integration of context-specific metadata, and the application of machine learning techniques to improve GEMs.