Accompanying modelling methods for dependent critical infrastructure networks’ interdependencies are explored in this paper with overtones to Bayesian networks, Network Centrality, Complex System Theory, Agent-Based Modelling, Graph Theory, and Simulation Modelling. This, together with network centrality information provides possible locations of failure in a network based on the most vital nodes. This theory which involves complex aspects and interaction of elements of the system inside is complex system theory. Bayesian networks ease probabilistic reasoning by allowing the example conditional independencies and incorporation of uncertainty. Agent-Based Modelling provides a micro-level picture of how system dynamics work as the different agents are created then allowed to act on their own. Graph theory has versatile instruments for analysing and describing the structural characteristics of networks, while parametric simulation enables testing of different options and measures in a risk-free environment. While there are so many merits accrued from these approaches, they also have demerit which include the following. Challenges regarding data availability and the quality of the data continue to plague accurate modelling and analysis. It is rather difficult to provide the accurate complex, temporal and asynchronous relationships between elements characteristic to critical infrastructures. These systems are dynamic, and as we have seen, complexity is magnified when the situation also fluctuates; hence, the need for dynamic models. This is another problem because the behaviours and interactions of the systems are unpredictable; often, this leads to requiring elaborate techniques for minimising the unpredictability. These constraints, therefore, must be addressed in order to develop more robust and reliable representations of the interrelationships of systems that make up a nation’s critical infrastructures.