Predictive maintenance is an increasingly adopted approach for managing bridge upkeep, as it enhances long-term resilience, reduces lifecycle costs, and improves safety. This thesis aims to develop a predictive maintenance framework for suspension bridges that, drawing on degradation models available in the literature, enables the identification of optimal maintenance strategies in terms of both safety and cost-effectiveness. First, aging models describing the loss of structural cross-section in bridge elements were examined. Then, two case studies were developed through detailed bridge modeling: one for a steel-deck bridge and another for a reinforced concrete–deck bridge. These case studies provided insight into the structural response to crosssection loss and supported the evaluation of different maintenance strategies to determine the most cost-effective solutions.