Objective: Rapid and accurate assessment of dynamic threats to surrounding assets, such as transmission lines, chemical storage tanks and nuclear facilities, during the propagation of forest-grassland wildfires is critical for emergency response and disaster mitigation. Traditional fire risk assessment methods typically rely on static hazard maps or single-time fire-front predictions and therefore fail to adequately account for the continuous spatiotemporal evolution of fire behavior influenced by changing environmental factors (e.g., wind and fuel), leading to cumulative error propagation in dynamic risk prediction. Furthermore, the complex and heterogeneous failure mechanisms of different infrastructure types are seldom incorporated into a unified, dynamically updating risk framework. Therefore, this study aims to develop a novel, integrated risk assessment method capable of continuously updating infrastructure failure probabilities as wildfire intensity and extent evolve. Methods: The proposed framework synergistically integrates a data-driven wildfire spread prediction model with a dynamic Bayesian network (DBN). The fire-affected landscape and key protected assets were first discretized into interconnected spatial nodes. A DBN structure was then constructed to capture two fundamental dependencies: the temporal autocorrelation of fire conditions at each node (how the state evolves) and the spatial dependencies between adjacent nodes (how fire spreads from one location to another). The core parameters of this DBN were informed by a data-driven fire spread model. Specifically, predictions of key fire behavior metrics, namely, spread rate and fireline intensity, were generated for each node. Then, DBN conditional probabilities were updated over time using the predicted spread rate and the fireline intensity, which were further mapped to asset-specific fragility models to infer evolving failure probabilities. Results: Using the proposed framework, we built a kilometer-scale synthetic fire scenario using every single protected asset and showed that the 24-h failure probabilities of transmission lines, chemical tanks, and nuclear facilities increased by up to 22%, 68% and 20% across wind-fuel scenarios. In addition, a case study based on observations from the 2022 Oak Fire (California, USA) was conducted using multiple protected assets. The 24-h failure probabilities reached 0.24 for transmission lines, 0.14 for hazardous-chemical tanks, and 0.53 for the nuclear facility. Furthermore, we evaluated a firebreak construction strategy: as the suppression effect on the rate of spread increased, the failure probability could be reduced to as low as 0 for transmission-line nodes and to 0.12 for the nuclear facility, whereas the probability reduction for hazardous-chemical tanks was not significant due to their spatial distribution and structural differences. Conclusions: This study proposes a novel dynamic wildfire risk assessment framework that integrates data-driven spread prediction with probabilistic graphical modeling. By continuously updating a DBN using fire behavior model outputs, the proposed approach effectively mitigates cumulative prediction errors in a spatiotemporally evolving environment and provides quantitative support for rapid response, emergency resource allocation, and intervention strategy assessment.