Forest fires increasingly threaten human lives, properties, and ecosystems, with climate change amplifying their size, intensity, and simultaneous occurrences. In South Korea, where forests cover over 60% of the land and wildland-urban interfaces are extensive, mitigating wildfire impacts requires accurate and timely fireline predictions to optimize firefighting resource allocation. While existing process-based propagation models provide rough estimates, they face limitations in capturing the complex dynamics of wildfire behavior influenced by weather, fuel, and topography. Additionally, the scarcity of time-series fireline observations and data on firefighting interventions hinders the development of AI-driven predictive models. This study introduces a hybrid wildfire propagation model that integrates process-based algorithms with AI techniques. The system calculates the rate of spread (ROS) and fireline movement using a process-based approach, while neural networks refine model parameters using 5-meter-resolution topography, forest type maps, and hourly weather data. The model generates predictions at 1-minute intervals and is trained with diverse loss functions to assimilate process-based parameters, ROS calculations, and historical fireline data from 27 wildfire events. Validation on five wildfire cases demonstrated the hybrid model’s improved performance over traditional process-based models, achieving Intersection Over Union (IOU) scores ranging from 0.4 to 0.6, with an average improvement of 0.14. These results highlight the potential of the hybrid model to enhance prediction accuracy and bridge the gap between conventional and advanced modeling methodologies. Future work will focus on expanding the training dataset and refining the model to address uncertainties in ROS predictions caused by firefighting interventions.