Artificial Intelligence-Driven Environmental Toxicology: Predictive Toxicity Modelling, Forensic Pollution Analysis, and AI-Enabled Public Health Surveillance

Abstract

Rapidly advancing technologies, including artificial intelligence, are needed to tackle environmental health issues. Traditional environmental toxicology has relied on experimental bioassays, epidemiological studies, and statistical models. However, the increasing volume and complexity of environmental data are not well served by existing frameworks. Artificial intelligence and machine learning provide new opportunities to improve environmental toxicology modelling and health surveillance. This article analyses the most promising artificial intelligence and machine learning techniques for modelling environmental toxicology, with a focus on forensic environmental studies and health risk assessment. The article reviews the state of the art in predictive toxicology modelling, quantitative structure-activity relationship models, deep learning, and large-scale data frameworks. In addition, the article explores environmental learning models to identify sources and patterns in environmental data, including the integration of forensic data to support the legal use of environmental data. This study also addresses the use of artificial intelligence in environmental epidemiology, the application of smart technologies in epidemiology, and the assessment of population exposure to environmental toxins. The review discusses key issues such as data negligence, model explainability, legal acceptability, ethical issues, regulatory impediments, and emerging research pathways, including hybrid mechanistic-AI models, federated learning, multi-omics, and global AI-driven monitoring. This review highlights the scope of AI-Environmental Toxicology for improving scientific accuracy, forensic accountability, and proactive data-driven public health advocacy.

Introduction

Environmental toxicology is the study of how chemical, physical, and biological pollutants affect ecosystems and human health. Rapid industrialization, urbanization, and technological growth have increased the release of harmful contaminants such as heavy metals, pesticides, microplastics, endocrine disruptors, and persistent organic pollutants (POPs). These pollutants spread through air, water, soil, and the food chain, where they can bioaccumulate and biomagnify, leading to long-term exposure in humans. Such exposures are linked to serious health problems, including cancer, neurodegenerative diseases, reproductive and developmental disorders, and metabolic conditions.

Traditional toxicology methods—such as laboratory testing, animal studies, and statistical modelling—have provided valuable insights but are costly, time-consuming, ethically limited, and inadequate for analyzing the large number of emerging chemicals and complex pollutant mixtures. They also struggle with identifying pollution sources, assessing liability, and predicting real-time environmental threats.

Artificial Intelligence (AI) and Machine Learning (ML) offer advanced solutions for predictive toxicity modelling, rapid hazard classification, exposure assessment, and environmental monitoring. Techniques such as support vector machines, random forests, and deep neural networks can improve risk prediction and support forensic investigations. Explainable AI enhances transparency and legal credibility, while integration with satellite data, biosensors, and omics technologies strengthens monitoring and early-warning systems.

The text also explains key toxicological concepts, including exposure routes, toxicokinetics (ADME processes), toxicodynamics, biomarkers, bioaccumulation, biomagnification, and risk assessment frameworks. Traditional risk assessment methods are being improved through AI-driven probabilistic modelling and real-time data analysis to better address complex environmental risks.

Overall, the paper highlights the integration of Environmental Toxicology, Data Science, Forensic Science, and AI to improve health risk assessment, environmental monitoring, and pollution source identification in modern data-driven systems.

Conclusion

The combination of artificial intelligence (AI) and machine learning (ML) with environmental toxicology is revolutionising the field by reshaping the processes of detection, interpretation, and management of environmental dangers. This review outlines the present state of the field. It demonstrates that, in the AI paradigm, predictive toxicological modelling improves predictive performance, enhances the efficiency of chemical risk evaluation, supports forensic source attribution, and supports public health surveillance initiatives. AI systems, when used in conjunction with the other methodologies, provide the most thorough risk assessment and the most advanced predictive capacity. Despite the obstacles, the most substantial research gaps remain unaddressed. The forensic and regulatory community is still developing standardised frameworks for validating AI-based toxicology models. The scarcity of uniform, high-quality datasets and the fact that models, not the data, comprise the geographic and demographic framework limit the generalizability of models. Moreover, achieving a balance between scientifically rigorous toxicology and legally defensible AI will require considerable refinement of methodologies to integrate the two. Implementing AI technologies in environmental toxicology also has significant potential to improve environmental governance, legal analytics, and public health planning. AI technologies can be employed for predictive modelling, population-level risk assessment and analytics, and risk exposure analytics. These tools can help decision makers and provide the basis for evidence-based policymaking and targeted intervention strategies. AI, when used in a principled and legally compliant manner, will provide the tools for a flexible, robust approach to protecting the public\'s health and to forensic accountability for the environment.

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Copyright

Copyright © 2026 Dr. Alay Maheta , Dr. Parvesh Sharma, Dr. Kiran Dodiya, Dr. Kapil Kumar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.