Green toxicology only becomes beautiful through AI

Green Toxicology extends the principles of Green Chemistry by embedding toxicological foresight into chemical design, with the aim of preventing hazards before substances reach markets or the environment. Its conceptual pillars-prevention, precaution, life-cycle thinking, and avoidance of regrettable substitutions-align closely with sustainability agendas such as the European Green Deal and the United Nations Sustainable Development Goals. Despite its promise, Green Toxicology has remained largely aspirational, limited by fragmented data, slow regulatory uptake, and reliance on new approach methodologies (NAMs) that still face validation and acceptance hurdles. Artificial intelligence (AI) offers a transformative solution by integrating heterogeneous datasets, enhancing predictive accuracy, and enabling probabilistic risk assessment. Deep learning, natural language processing, and explainable AI can mine legacy studies, link adverse outcome pathways, and design safer chemistries proactively. Coupled with microphysiological systems and omics, AI makes Green Toxicology predictive, human-relevant, and scalable. Together, they form a practical framework for guiding chemical innovation toward sustainability, reconciling industrial productivity with ecological integrity and public health protection.