Traditional bridge maintenance approaches often lack the capability to capture interactions among structural elements or assess the vulnerability of freight networks to disruptions such as bridge closures. To overcome these limitations, this study introduces a data-driven bridge maintenance framework that integrates artificial intelligence, geographic information systems, and graph-theory-based network modeling. A comprehensive graph-based network representing Georgia’s National Highway Freight Network was developed using geospatial coordinates and roadway intersection data to evaluate structural and topological criticality at the network level. Structural condition forecasting of individual bridges was conducted using advanced recurrent neural network (RNN) models, including long short-term memory and Gated Recurrent Unit architectures. These models predict future deck condition ratings based on historical element data, average daily truck traffic, age, maximum span length, and the number of main-unit spans. The integrated RNN-driven, graph-theory-based framework uncovers key patterns influencing bridge performance and identifies topological weaknesses that may compromise freight mobility. This analysis enables risk-informed prioritization of maintenance, repair, and replacement strategies, supporting a shift from reactive to predictive decision-making and enhancing the resilience of critical transportation infrastructure. Findings highlight the framework’s utility in evaluating the impacts of disruptive events on bridge closures and network accessibility.