China has a vast bridge infrastructure inventory that, on the whole, has entered a phase marked by intensive performance degradation and centralized maintenance. Traditional operation and maintenance paradigms, which heavily rely on manual labor, find it challenging to handle systemic safety assurance pressures under complex service environments. This study delves into the evolution trajectory of four types of intelligent technology paradigms in bridge engineering: logical models, discriminative models, generative models, and agent-based systems. It also reviews relevant research advances of the paradigms. Logic-based bridge engineering intelligence covers bridge sensing technologies and bridge health monitoring systems; discriminative bridge-engineering intelligence encompasses bridge-deck moving-load monitoring, component anomaly identification and defect detection, bridge monitoring data auditing, and structural numerical simulation with intelligent computing; and generative bridge-engineering intelligence involves applications of large language models, geometric model construction, and numerical-analysis mesh generation. Future directions are further discussed, including lightweight edge intelligence, domain-specific multimodal large models, full-lifecycle knowledge graphs, engineering swarm intelligence and embodied intelligence, and agent-based foundations for predictive maintenance. The study concludes that existing intelligent methods and technologies remain insufficient to support highly reliable engineering decision-making in complex service environments and cannot yet meet the growing demands for bridge safety, resilience enhancement, and service-life extension. Accordingly, research and development of agent-based systems for bridge engineering should be prioritized and their large-scale deployment promoted, thus to upgrade the capabilities of intelligent technologies in bridge engineering and advance the high-quality development of transportation infrastructure.