This study investigates the effectiveness of the DistilBERT model in classifying tweets related to disasters. This study achieved significant predictive accuracy through a comprehensive analysis of the dataset and iterative refinement of the model, including adjustments to hyperparameters. The benchmark model developed highlights the benefits of DistilBERT, with its reduced size and improved processing speed contributing to greater computational efficiency while maintaining over 95% of BERT's capabilities. The results indicate an impressive average training accuracy of 92.42% and a validation accuracy of 82.11%, demonstrating the practical advantages of DistilBERT in emergency management and disaster response. These findings underscore the potential of advanced transformer models to analyze social media data, contributing to better public safety and emergency preparedness.