<p>Wildfires pose an escalating threat to critical infrastructure, particularly transmission lines, leading to severe power outages and significant economic impacts. While existing studies have primarily focused on static risk assessment methods, this research introduces a novel dynamic risk assessment framework that addresses the rapidly evolving nature of wildfire dynamics through advanced data assimilation techniques, utilizing a real-world wildfire case study. Unlike previous approaches that rely on single-parameter updates or static fire line predictions, our framework integrates observational data into the wildfire simulation tool FARSITE using an ensemble transform Kalman filter, enabling multi-parameter updates that significantly enhance the predictive accuracy of fire line positions and their associated uncertainties. Furthermore, a Monte Carlo simulation-based approach is developed to dynamically calculate wildfire arrival probabilities, combined with a robust quantitative framework for assessing transmission line failure likelihood under fire scenarios. The fire line intensity, determined under the worst-case scenario principle, serves as the input for the quantitative assessment framework. By integrating wildfire arrival probabilities and transmission line failure risks, this study provides a comprehensive and dynamic risk assessment tool, offering a transformative perspective on managing the interface between wildfires and critical infrastructure.</p>