The emergence of systems, synthetic biology, and meta-omics approaches has advanced the exploration of intricate cellular processes in biological systems. A living system’s flux map reflects an integrated functional phenotype resulting from numerous levels of biological organization and regulation, such as the genome, transcriptome, and proteome. Constraint-based models (CBM) employ metabolic reaction network models of metabolism that operate in a steady state, ensuring that reaction rates (fluxes) and amounts of metabolic intermediates remain constant. Constraint-based models (CBM) employ metabolic reaction network models of metabolism that operate in a steady state, maintaining constant reaction rates (fluxes) and stable levels of metabolic intermediates, while providing estimated (metabolic flux analysis, MFA) or anticipated (flux balance analysis, FBA) fluxes through the network in vivo that cannot be computed directly. These fluxes can provide insight into basic biology and are being effectively utilized to drive metabolic engineering efforts. Furthermore, the rapidly evolving discipline of machine learning (ML) and its specialized branch, deep learning (DL), provide critical computational frameworks for decoding complicated and diversified biological data. Despite this, the emergence of these multidisciplinary methodological frameworks is largely independent, limiting the concatenation of biological information from many disciplines. As a result, we have proposed the possibility of combining multidisciplinary tools and methodologies from many domains, such as CBM, omics, and machine learning, to investigate biochemical phenomena transcending conventional biology dogma. Genome-scale metabolic models (GEMs) remain effective tools used to comprehend metabolism at the systems level. GEMs, in their most basic form, do not take into account cellular regulation. It is worth noting that several strategies are focused on directly integrating omics data into GEMs in order to improve model accuracy. The GEM reconstruction techniques and regulatory systems that control metabolism are also emphasized.