This review article discusses the application of genome-scale metabolic models (GEMs) in deciphering and designing microbial communities. GEMs provide a comprehensive framework for understanding the complex interactions within microbial communities, revealing their intricate metabolic relationships and ecological properties. The article outlines the evolution of GEMs from single-strain to community-level models, emphasizing the importance of integrating environmental and intracellular resources in shaping microbial community assembly. It also highlights the challenges and future directions of GEMs, including the integration of quorum sensing, microbial ecology interactions, machine learning algorithms, and automated modeling for consortia-based applications. The review summarizes key developments in GEMs, including the construction of static and dynamic community-level GEMs. It discusses the role of external environmental and intracellular resources in microbial community assembly and the challenges in obtaining parameters and biological interpretability. The article also explores the application of GEMs in various fields, such as synthetic biology, environmental restoration, and healthcare, and emphasizes the need for multi-omics data integration to enhance the accuracy of microbial community predictions. The review highlights the importance of developing standardized processes, computational tools, and evaluation criteria for modeling non-model and uncultured microbial strains. It also discusses the challenges in reconstructing microbial community models, including the need for open resource-sharing platforms and the development of reproducible fabricated ecosystems (EcoFABs) for functional modeling. The article concludes by emphasizing the potential of GEMs in advancing synthetic biology and microbial ecology, while also addressing the limitations and future directions of GEMs in capturing complex microbial interactions.This review article discusses the application of genome-scale metabolic models (GEMs) in deciphering and designing microbial communities. GEMs provide a comprehensive framework for understanding the complex interactions within microbial communities, revealing their intricate metabolic relationships and ecological properties. The article outlines the evolution of GEMs from single-strain to community-level models, emphasizing the importance of integrating environmental and intracellular resources in shaping microbial community assembly. It also highlights the challenges and future directions of GEMs, including the integration of quorum sensing, microbial ecology interactions, machine learning algorithms, and automated modeling for consortia-based applications. The review summarizes key developments in GEMs, including the construction of static and dynamic community-level GEMs. It discusses the role of external environmental and intracellular resources in microbial community assembly and the challenges in obtaining parameters and biological interpretability. The article also explores the application of GEMs in various fields, such as synthetic biology, environmental restoration, and healthcare, and emphasizes the need for multi-omics data integration to enhance the accuracy of microbial community predictions. The review highlights the importance of developing standardized processes, computational tools, and evaluation criteria for modeling non-model and uncultured microbial strains. It also discusses the challenges in reconstructing microbial community models, including the need for open resource-sharing platforms and the development of reproducible fabricated ecosystems (EcoFABs) for functional modeling. The article concludes by emphasizing the potential of GEMs in advancing synthetic biology and microbial ecology, while also addressing the limitations and future directions of GEMs in capturing complex microbial interactions.