Metabolic systems biology integrates computational and experimental approaches to understand the complex interactions within biological networks. This subject has revolutionized biotechnological applications, including drug discovery, biofuel production, and metabolic engineering. Functional genomics, through high-throughput omics technologies, enables the characterization of gene functions, regulatory mechanisms, and metabolic pathways into valuable datasets. Genome-scale metabolic models (GEMs) are primary candidates for using such datasets by offering a mathematical framework to study the metabolic capabilities of organisms. These models allow the simulation of cellular metabolism to identify metabolic pathways, predict the effects of genetic modifications, and optimize metabolites production. The integration of multiomics data into GEMs further enhances their predictive power, leading to the development of more robust and context-specific models tailored for specific applications. A few GEMs have been developed for specific BC producers and have demonstrated to be powerful tools to optimize substrate utilization, modify specific pathway, and perform strain improvement for cellulose biosynthesis. However, the integration of omics data and experimental validation of the identified targets remains limited. The continuous development of GEMs coupled with the integration of multiomics data, and the potential of machine learning approaches can drive further advancements in BC biotechnology, paving the way for more efficient and scalable production systems.