Food safety incidents cause serious threats to public health, requiring efficient detection systems. Thisstudy contributes to SemEval 2025 Task 9: Food Hazard Detection by leveraging insights from existing literature and using multiple BERT-based models for multi-label classification of food hazards andproduct categories. Using a dataset of food recall notifications, we applied preprocessing techniquesto prepare data and address challenges like class imbalance. Experimental results show strong hazardclassification performance on ensembled models such as DistilBERT, SciBERT, and DeBERTa but highlight product classification variability. Building on Nancy et al. and Madry et al.’s work, we explored strategies like ensemble modeling and data augmentation to improve accuracy and explainability, paving the way for scalable food safety solutions.