AI in forensic toxicology: Machine learning for detection, classification, and prediction of Illicit substances

Artificial intelligence (AI) and machine learning (ML) are increasingly transforming forensic toxicology by improving the detection, classification, and interpretation of illicit substances. Conventional analytical techniques such as gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS/MS) remain essential tools in toxicological investigations; however, the growing complexity of toxicological datasets and the rapid emergence of novel psychoactive substances (NPS) require more advanced computational approaches. Machine learning algorithms including support vector machines, random forests, artificial neural networks, and deep learning models have demonstrated strong potential for analyzing high-dimensional spectral data, identifying structural analogues, predicting metabolic pathways, and supporting toxicological interpretation. These approaches enable more efficient pattern recognition in mass spectrometry and metabolomics data, facilitating faster identification of emerging substances and improving analytical sensitivity. This review examines recent developments in the application of machine learning in forensic toxicology, focusing on its role in illicit substance detection, structural classification, and predictive toxicology. Key challenges, including data availability, model interpretability, reproducibility, and legal admissibility of AI-generated evidence, are critically discussed. Future perspectives such as explainable AI, federated learning, and integration with high-throughput analytical platforms are also highlighted. Overall, the integration of machine learning with forensic analytical workflows offers significant opportunities to enhance the accuracy, efficiency, and reliability of toxicological investigations.