The 2030 Agenda for Sustainable Development recognizes the indivisible and integrated nature of its 17 Sustainable Development Goals (SDGs) and 169 targets, as well as the need to address these interlinkages to fully achieve its aims. In addition, the Agenda stresses the importance of “leaving no one behind”, which can only be achieved by understanding the interlinkages between the Goals and by undertaking actions to bring them together for the benefit of all. Thus, the identification of these linkages will enable countries to implement the SDGs effectively by harnessing synergies between them while managing potential conflicts. Despite their significance in monitoring initiatives, indicators separately are not adequate to provide an insight into the complex cause and effect relations within global development issues. The suitability of Bayesian Networks (BNs) to integrate multiple and simultaneous relationships has been largely exploited in the literature. Taking a dedicated goal on water and sanitation (SDG 6) as starting point, this paper reviews the potential of a BNs approach to analyse the interdependency between the SDGs, the associated targets and the corresponding indicators. Available global data has been exploited to run the BNs model. Achieved results are compared with a recent research developed by UN-Water, where interlinkages between the targets under Goal 6 and other targets across the 2030 Agenda are conceptually described. The paper discusses the extent to which a BNs is a suitable system to identify and assess these linkages, relationships and synergies. The study concludes that a BNs approach is useful to accommodate the complexities and interdependencies of the SDGs targets and indicators.