The latest research on wildfire forecast and backtracking has adopted AI models, which require a large amount of data from wildfire scenarios to capture fire spread patterns. This paper explores the use of cost-effective simulated wildfire scenarios to train AI models and apply them to the analysis of real-world wildfire. This solution requires AI models to minimize the Sim2Real gap, a relatively brand-new topic in the research community of fire spread analysis. To investigate the possibility of minimizing the Sim2Real gap, we collect the Sim2Real-Fire dataset that contains 1M simulated scenarios with multi-modal environmental information for training AI models. We prepare 1K real-world wildfire scenarios for testing the AI models. We also propose a deep transformer network, S2R-FireTr, which excels in considering the multi-model environmental information for forecasting and backtracking the wildfire. S2R-FireTr surpasses state-of-the-art methods in the real-world scenarios of wildfire.