This study presents a multimodal data fusion framework for road damage detection and prediction, integrating RGB images, LiDAR point clouds, and GPR (Ground Penetrating Radar) data to enable high-precision detection of surface cracks, potholes, and underground voids, as well as dynamic trend forecasting. By leveraging Deep Mapping 2.0 and the RAFT algorithm, the alignment accuracy between RGB and LiDAR data was significantly improved, reducing registration error to 2.3 mm. Concurrently, the spatial mapping accuracy of GPR data was enhanced to 4.8 mm, ensuring precise multimodal data fusion. A Cross-Attention Transformer combined with a Feature Pyramid Network (FPN) was used for dynamic feature weighting, achieving a crack detection IoU of 97.3% and an AP@0.5 of 93.7% for underground void detection, thereby substantially enhancing the model's performance in detecting complex road damage. Moreover, a trend prediction model integrating ConvLSTM and a spatiotemporal attention mechanism achieved an MAE of 8.7% in a six-month damage trend prediction experiment, reducing prediction error by 34% compared to existing methods, underscoring the model's effectiveness in forecasting damage progression.The experimental results demonstrate that the proposed framework exhibits strong adaptability and stability across diverse road damage detection tasks, particularly excelling in the joint detection of cracks and underground voids with high accuracy. Furthermore, the framework is readily extendable to infrastructure health monitoring applications, such as bridges and tunnels, providing robust technological support for intelligent road maintenance and offering data-driven insights for the long-term optimization and sustainability of urban transportation infrastructure.