Metabolic pathways regulate essential biochemical processes in biological systems, and their modeling has provided fundamental tools for understanding cellular metabolism and its applications in fields such as precision medicine, biotechnology, and metabolic disease diagnostics. Over the years, numerous mathematical and computational models have been developed to describe these networks, employing approaches ranging from flux balance analysis to dynamic systems and data-driven methods. This work presents an overview of existing models for describing metabolic pathways, emphasizing their role in representing biochemical processes and their scientific and industrial applications. Building on this review, new perspectives are explored regarding the integration of metabolic models with innovative experimental data, particularly physiological signals that may contain implicit information yet to be validated. One specific case involves the use of surface electrical currents measured on human skin, which could provide insights into the metabolic demand of an organism and require validation through existing modeling frameworks. Furthermore, the potential for integrating metabolic models with advanced computational approaches is analyzed, with a focus on their contribution to the development of Human Digital Twins. These models enable “what-if" simulations to predict the effects of metabolic variations in a controlled virtual environment. This perspective opens new opportunities for research and applications in metabolic modeling, with implications for diagnostics, personalized therapies, and the development of new biomedical analysis tools.