This publication explores the use of machine learning techniques in evaluating toxicological risks associated with chemical compounds present in food systems.

The study applies predictive modeling to assess exposure levels and potential health risks, utilizing data from laboratory experiments and environmental monitoring sources. Various algorithms were tested to improve the accuracy of toxicity predictions and identify high-risk compounds.

The results indicate that machine learning models can significantly enhance the efficiency of toxicological assessments by providing faster and more reliable analysis compared to traditional methods.

This research supports the advancement of data-driven toxicology and contributes to improving regulatory decision-making processes in food safety management.