Ensuring food safety requires effective detection of potential hazards in food products. This paper presents the participation of VerbaNexAI in the SemEval-2025 Task 9 challenge, which focuses on the automatic identification and classification of food hazards from descriptive texts. Our approach employs a machine learning-based strategy, leveraging a Random Forest classifier combined with TF-IDF vectorization and character n-grams (n=2-5) to enhance linguistic pattern recognition. The system achieved competitive performance in hazard and product classification tasks, obtaining notable macro and micro F1 scores. However, we identified challenges such as handling underrepresented categories and improving generalization in multilingual contexts. Our findings highlight the need to refine preprocessing techniques and model architectures to enhance food hazard detection. We made the source code publicly available to encourage reproducibility and collaboration in future research.