Evaluating the reliability of complex network systems is crucial to the efficiency of their operations and the continuity of applications in sectors of strategic importance (e.g., aviation, telecommunications, and energy), where failures may result in significant financial losses and endanger the affected systems’ valuable assets and infrastructure. Traditional approaches commonly rely on simplified suppositions which cannot adequately reflect the dynamic relationships between the components. Therefore, advanced analytical models, such as the incidence matrix, have been used as powerful tools in comprehensive reliability assessment. The paper emphasizes the importance of the incidence matrix for improving reliability assessment accuracy and computational speed. The study proposed an integrated model that accounts for multiple failure cases, thereby enabling more robust reliability estimation methods. It is vital to evaluate the reliability of complex network systems. Furthermore, the paper considers the potential fusion of modern methods, including artificial intelligence, with the incidence matrix to enhance predictive power. It has been shown that incidence matrix-based approaches successfully address the complexity of electrical networks and real-time systems such as railways or maritime networks. These results highlight the effectiveness of the methodology for hyperspace mapping of critical subsystems, thereby improving reliability management. It also outlines directions for future research that will focus on integrating novel computational methods to enhance the methodology for analyzing the reliability of complex network systems.