The increasing complexity of these data and their related interactions (e.g., interactions between biological systems, genes, proteins, and neurons) requires an appropriate approach, which is realized through network models capable of systematically analysing the relationships between entities within a multi-level interconnected system. Multilayer networks best represent this approach. The workflow begins with exploring the structural and functional aspects of a multilayer network, shedding light on advanced techniques for model analysis, management, and visualization. Although various approaches have been implemented, the study of these complex structures presents several open challenges. Through a synthesis of theoretical insights and empirical observations, a comprehensive understanding of how multilayer networks model and respond to various stimuli is provided; however, several limitations related to data accessibility and computational costs are highlighted. It is also considered, the application of multilayer networks in the field of neuroimaging, aiming to leverage the available data from neuroimaging studies to understand brain connectivity and function better. The final objective involves designing and developing methodologies that enable the graphical representation of multilayer networks, the research and analysis of the topological metrics that comprise the network, to facilitate the evolution of medicine in the realm of data-driven healthcare.