Prestressed concrete (PC) continuous beam bridges are widely used in transportation infrastructure. However, their construction involves substantial material consumption, raising sustainability concerns amid increasing environmental pressures. This study aims to address the urgent need for resource-efficient bridge design by developing a comprehensive optimization framework that minimizes material usage while ensuring structural safety, durability, and compliance with engineering standards. The proposed methodology integrates a Genetic Algorithm (GA) with a Backpropagation (BP) neural network to optimize both the cross-sectional geometry and the overall alignment of PC continuous beam bridges. The GA is utilized to identify optimal cross-sectional parameters within regulatory constraints, while the BP neural network, trained on extensive design data, refines the bridge bottom height profile to enhance structural performance. The integrated GA-BP framework is validated through a case study of a continuous beam bridge, demonstrating a 94% improvement in design efficiency, a 14% reduction in concrete consumption, and a 34% reduction in prestressed steel usage during the preliminary design stage. These results highlight the framework’s significant potential in advancing sustainable and intelligent bridge design, offering a novel approach to combining artificial intelligence with structural optimization for practical engineering applications.