This paper presents a hybrid approach to food hazard detection for SemEval-2025 Task 9, combining traditional machine learning with advanced language models. For hazard classification (Sub-Task 1), we implemented a novel ensemble system integrating XGBoost with fine-tuned GPT-2 Large and LLaMA 3.1 1B models. For vector detection (Sub-Task 2), we employed a prompt-engineered approach using Flan-T5-XL, highlighting challenges in exact vector matching. Our analysis demonstrates the effectiveness of combining complementary models while revealing opportunities for improvement in rare category detection and extraction precision.