Environmental degradation has emerged as one of the most powerful — and preventable — drivers of ill health in the 21st century. Air pollution, unsafe water, toxic waste exposure and climate-induced heat stress are no longer peripheral ecological concerns; they are central public health challenges. An estimate using data from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019, published in The Lancet Planetary Health in 2022, found that pollution remains responsible for approximately 9 million deaths per year, corresponding to one in six deaths worldwide. Air pollution alone causes over 6·5 million deaths each year globally, and this number is increasing. India, with its dense population and uneven environmental governance, bears a disproportionate share of this burden.
Against this backdrop, Artificial Intelligence (AI) is increasingly being deployed as a tool to strengthen environmental health systems — enabling real-time monitoring, early disease prediction and fine-grained mapping of environmental injustice. Yet, while AI promises a shift from reactive to predictive governance, it also raises serious ethical concerns related to surveillance, bias and accountability. The central policy question is not whether AI should be used, but how it should be governed.
The IndiaAI Mission
At the centre of this transformation is the IndiaAI Mission, approved by the Government of India in March 2024 with an outlay of ₹10,372 crore. The Mission seeks to build a robust national AI ecosystem — democratising access to data, strengthening indigenous capability and embedding safeguards for safe and trusted AI. Its seven strategic pillars include expansion of compute capacity (with over 10,000 GPUs being deployed), creation of open and high-quality datasets, development of foundational models, startup financing, skills enhancement, and governance frameworks for responsible AI.
Conventional environmental regulation remains largely reactive: pollution is measured after legal thresholds are breached, and health systems respond once disease burdens rise. AI fundamentally alters this model by enabling anticipatory risk assessment.
Research published in Scientific Reports (2024), demonstrates that machine-learning models integrating satellite imagery, ground sensors, meteorological data, traffic density and industrial emissions can forecast air quality in Indian cities such as Delhi, Chennai, Kolkata and Hyderabad with prediction accuracies ranging from 89% to 98% for PM₂.₅ and nitrogen dioxide. These models significantly outperform traditional regression-based approaches and allow authorities to anticipate pollution spikes days in advance.
The public health relevance of such forecasting is stark. According to the Air Quality Life Index (AQLI) developed by the Energy Policy Institute at the University of Chicago 2023, air pollution reduces average life expectancy in India by 3.5 years. AI-enabled air quality forecasting, therefore, represents not merely technological innovation, but a critical preventive health intervention.
Disease prediction
Environmental exposure and disease are deeply intertwined. Heatwaves increase cardiovascular mortality, polluted air worsens respiratory illness, and contaminated water drives gastrointestinal and renal diseases. AI systems are uniquely suited to analysing such multi-factorial relationships across large datasets.
A recent synthesis published in the journal Mathematics (2025) by MDPIshows that AI-assisted disease surveillance systems — integrating environmental sensors, hospital admissions, pharmacy sales, wastewater surveillance and mobility data — can detect infectious disease outbreaks with over 90% accuracy, often earlier than conventional reporting systems.
One of the most widely cited global examples is HealthMap, developed by researchers at Boston Children’s Hospital, which aggregates digital disease signals from media reports, official alerts and online sources to provide near real-time outbreak mapping.
India has begun adopting similar approaches. AI-enabled disease surveillance tools ‘Health Sentinel’, integrated into national monitoring systems have generated thousands of early alerts, reducing detection delays and improving coordination among public health authorities.
At a more local scale, the application of AI in Andhra Pradesh’s Uddanam region— a known hotspot for chronic kidney disease of unknown origin — is particularly instructive. As published by Scientific Reports (2025) an AI model trained on regional clinical and environmental datasets achieved nearly 99% accuracy in early CKD prediction, offering a crucial diagnostic advantage in a region where conventional screening has struggled and environmental causation remains under investigation.
Making inequality visible
Environmental risks are rarely distributed evenly. Informal settlements, industrial workers and marginalised communities are consistently exposed to higher pollution levels while receiving fewer health protections. AI offers tools to quantify and visualise these inequitieswith unprecedented precision.
Research published in Environmental Research Letters (2023) demonstrates how geospatial AIcan combine pollution exposure data with socio-economic indicators such as income, housing quality and access to services to reveal systematic environmental injustice across urban landscapes.
In India, this potential is beginning to materialise. Recently, Indian cities have been reported using AI-enabled satellite analysisto map urban heat vulnerability at neighbourhood and even building levels, revealing sharp intra-city disparities that conventional ward-level data failed to capture.
Globally, initiatives such as Climate TRACE (2023),use AI to identify major pollution “super-emitters” across sectors and geographies. By making emissions data transparent and accessible, such platforms strengthen public accountability and regulatory enforcement.
Ethical fault lines
Despite its promise, AI’s application to environmental health raises serious ethical risks.
Surveillance and privacy:Many AI systems rely on sensitive health and location data. When environmental monitoring is combined with individual-level information, the risk of intrusive surveillance increases. Without robust consent frameworks and data protection safeguards, public trust may be undermined.
Algorithmic bias:A report published by the European Union Agency for Fundamental Rights (2022) and a study published in Nature Human Behaviour (2025) caution that AI systems trained on historically incomplete or biased datasets can systematically underestimate risks in marginalised communities. If pollution monitoring has been sparse in informal settlements or rural areas — as is often the case in India — AI outputs may reinforce existing inequalities rather than correct them.
Environmental footprint of AI:Ironically, AI itself is not environmentally neutral. A study published by UC Riverside Caltech using a U.S. Environmental Protection Agency model, estimates that air pollution linked to data-centre emissions has already imposed billions of dollars in public health costs globally, disproportionately affecting low-income communities located near such infrastructure.
To ensure AI strengthens environmental health rather than undermines it, India must treat these systems as public infrastructure, subject to democratic oversight. This requires strong data protection laws, transparent and auditable AI models, bias testing and equity metrics, participatory system design involving affected communities, and environmental accountability for AI infrastructure itself.
A cautious opportunity
AI offers India a powerful opportunity to transform environmental health governance — shifting from crisis response to anticipation, from averages to granularity, and from invisibility to accountability. But technology alone cannot deliver justice. Whether AI becomes a force for healthier lives or a new layer of exclusion, will depend on how consciously it is governed. In a country where environmental risks and social vulnerabilities intersect so sharply, intelligence without ethics would be a costly mistake.
(Dr. Sudheer Kumar Shukla is an environmental scientist and sustainability expert. He currently heads the think tank, Mobius Foundation in New Delhi. sudheerkrshukla@gmail.com. Dr. Harveen Kaur is an environmental sustainability and policy expert. dr_harveen@outlook.com)