<p>This study proposes a comprehensive performance evaluation and intelligent decision support system for the maintenance and seismic retrofitting of aging transportation infrastructure, aimed at enhancing structural safety, extending service life, and optimizing life-cycle costs. The research focuses on reinforced concrete (RC) bridge columns commonly found in urban elevated railway systems in Japan, addressing key issues such as strength degradation, insufficient ductility, and inadequate seismic performance. Using static nonlinear analysis, the residual load-bearing capacity and damage state of the columns were evaluated, and a comprehensive performance index system was established. To enhance structural resilience while minimizing operational disruption, a space-efficient seismic reinforcement method characterized by high spatial adaptability was adopted, making it particularly suitable for dense urban environments. The decision-making process is underpinned by the Adaptive Integrated Digital Architecture Framework (AIDAF), which establishes a closed-loop system integrating data acquisition, performance assessment, parameter optimization, and feedback validation. By incorporating machine learning (ML), specifically the random forest (RF) algorithm, into the AIDAF framework, a data-driven retrofitting system was developed. Feature importance analysis identified key variables, including steel plate thickness, rebar diameter, and spacing. The ML-enhanced system reduces design iteration time and facilitates rapid evaluation of multiple reinforcement configurations. The predictive accuracy of the model was validated using an in-service railway viaduct, confirming its effectiveness. Furthermore, the study recommends integrating explainable AI techniques to improve transparency and regulatory acceptance. The findings demonstrate that the proposed ML-AIDAF framework is technically feasible, economically viable, and scalable for real-world infrastructure retrofitting projects. </p>