The development of polymer nanocomposites with enhanced electrical conductivity is of considerable interest for advanced technological applications such as sensing, electromagnetic shielding, and printed electronics. In this context, the formation of electrically conductive networks is critical to the functional performance of these materials. Incorporation of multilayer graphene sheets (MLGs) into polymeric matrices enables the formation of three-dimensional conductive networks, characterized by significant increases in electrical conductivity and associated with percolation phenomena. However, a detailed understanding of the factors governing the morphology and connectivity of these networks is still incipient. Previous studies have suggested that mesoscale network connectivity is one of the key factors influencing the conductivity of composite material. This work investigates the formation of electrical percolation networks in MLG/epoxy nanocomposites through an experimental study of the electric domains based on the analysis of images acquired via optical microscopy and electrostatic force microscopy (EFM). Specimens with several MLG concentrations were prepared using a three-roll mill calender. Electrical characterization was performed through direct current conductivity measurements. The electrical domains were evaluated at the microscale (optical) and nanoscale (EFM), providing insights into the local conductive behavior and network formation. The analysis of network connectivity was evaluated using a custom-developed software tool for image processing and quantification of structural network metrics. The results indicate that network conductivity and electrical conductivity are strongly influenced by the density of agglomerates and their degree of interconnection. Topological parameters used to characterize the networks were found to correlate directly with the electrical conductivity of the nanocomposites. Such a correlation highlights the potential of quantitative multiscale analyses to characterize and predict the behavior of electrically conductive networks in graphene-based composites.