Our approach for the SemEval-2025 Task 9: Subtask 1, The Food Hazard Detection Challenge showcases a robust ensemble learning methodology designed to classify food hazards and associated products from incident report titles. By incorporating advanced data augmentation techniques, we significantly enhanced model generalization and addressed class imbalance through the application of focal loss. This strategic combination led to our team securing the Top 1 position, achieving an impressive score of 0.8223, underscoring the strength of our solution in improving classification performance for food safety risk assessment.