Artificial intelligence (AI) is being used by Indian meteorological organisations more and more to improve early warning systems and forecast heatwaves. The majority of the work to date has gone toward enhancing weather models that can forecast when temperatures are expected to surpass hazardous levels and assist authorities in making preparations ahead of time.
However, experts believe the discussion is starting to move beyond forecasting alone and toward whether AI might also help cities handle high heat more successfully on the ground as heatwaves become more frequent, lengthy, and fatal.
Given that India frequently experiences heat stress, particularly in urban areas, the matter has grown more pressing.In numerous areas this month, temperatures have already risen above 45 degrees Celsius due to the current pre-monsoon heat wave.
The toll on health is also becoming apparent. At least 16 heatstroke-related deaths were reported in Telangana earlier this month, and Andhra Pradesh saw an increase in heat-related ailments as vulnerable populations continued to be impacted by extended exposure to severe temperatures.
This is where AI's next role is being tested: not just forecasting heat, but also assisting cities in making decisions before it becomes lethal.
"Forecasting is only the first layer of heat resilience," stated Vijay Sampathkumar, chief business officer of Refroid Technologies, a company that creates cooling systems for data centers, AI, and HPC.

According to experts, AI technologies are being utilised more and more to detect urban heat islands, enhance the planning of cooling infrastructure, and bolster healthcare readiness. The emergence of hyperlocal heat mapping, which uses artificial intelligence (AI) to identify vulnerable communities at the street or building level by analysing satellite data, land-use patterns, vegetation cover, and building density, is one of the most significant advancements.
According to Professor Anjal Prakash of FLAME University's college of public policy, "AI's most significant contribution goes beyond forecasting and is converting hazard information into precise local actions that reduce harm."
As Indian towns struggle with the escalating effects of urban heat islands, he said that high-resolution heat and sensitivity maps can assist authorities in prioritising measures like planting trees, cool roofs, reflective pavements, and shaded public areas.

GIS and GeoAI technologies are increasingly assisting authorities in determining where such measures could have the greatest cooling impact, according to Agendra Kumar, managing director of Esri India.
According to Kumar, "GIS models can overlay land surface temperature data with green cover and building density to pinpoint exactly where a cool roof, tree corridor, or ventilation pathway can deliver the highest cooling impact."
AI is increasingly being used in utilities management and healthcare readiness.
According to Prakash, "AI can fuse climate data with demographic and health facility information to flag wards at the highest risk of heat-related illness in the health sector." Such systems could assist authorities in prepositioning ambulances, bolstering hospital readiness, and delivering targeted public health messaging prior to temperatures peaking.Another crucial issue that is becoming more important is electricity management. On May 21, India's peak electricity consumption reached a record 270.82 gigawatts (GW) during daylight solar hours as rising temperatures increased the need for cooling in a number of areas.
AI technologies are increasingly assisting cities in predicting cooling demand and anticipating stress on electrical grids prior to blackouts, according to Vasudha Madhavan, founder and CEO of climate-tech investment banking business Ostara Advisors.
"Proactive, data-driven heat resilience planning is the bigger shift from reactive response," she stated.
Additionally, experts cited new technologies like digital twins, which are virtual versions of urban systems that let authorities model heat stress situations before taking corrective action.

The difficulty outside of metro areas
The next difficulty, according to experts, is expanding these technologies outside of urban India, even though the majority of AI-led climate systems are currently concentrated in larger cities.
According to Sampathkumar, "scaling climate technologies to tier-II, tier-III, and rural regions is both a technological and governance challenge."
Smaller towns and rural areas continue to face significant obstacles due to poor internet connectivity, inaccurate climatic datasets, sparse sensor networks, and low institutional capability. This has forced businesses to adopt mobile-first platforms, low-bandwidth technologies, and local-language alerts.
According to Prakash, a number of groups were also investigating community-based dissemination systems incorporating ASHA employees, panchayats, and local radio networks, as well as multilingual SMS alerts and offline-capable applications.
Examining the problem through the prism of digital inequality makes it much more difficult.Climate and Sustainability Initiative expert Janhavi Bhujabal cautioned that if AI-driven climate systems ignore vulnerable communities and poor last-mile connection, they run the risk of exacerbating already-existing disparities.
"A migrant worker from Tamil Nadu or West Bengal in the Hindi heartland who receives information in his or her non-native language runs the risk of experiencing both ex ante and ex post delays in response to early warning alerts and institutional support," she stated.
The issue cannot be resolved by technology alone
Experts warned that although AI systems are developing quickly, technology cannot address India's heat sensitivity on its own.
The largest obstacles, according to Anupam Shrey, founder of the AI climate risk assessment company Plutas.ai, were not just technological.Additionally, he emphasised the "indoor heat blind spot," which is especially prevalent in informal communities where indoor temperatures can stay dangerously high even after dark.
"Indoor heat in informal housing remains systematically undercounted in official risk assessments, despite rising healthcare costs, collapsing productivity, and declining well-being," he stated.
Additionally, experts cautioned about relying too much on AI systems in areas with poor infrastructure and data quality.
According to Shrey, "false precision is the specific danger in heat resilience contexts." "A model trained on sparse or biased data can actively misdirect resource allocation if it produces a neighborhood-level heat risk score with high apparent confidence."
Prof. Prakash went on to say that reliance on digital infrastructure and cloud connectivity could become a liability in times of emergency."An AI-centric system can fail precisely when it is most needed, and power or network outages often coincide with crises," he stated.
Bhujabal contended that rather than viewing AI-led climate resilience systems as primarily technological endeavours, authorities have to view them as a public good.
Instead, she said, "policymakers should treat them as a public good that combines climate science, governance, internet access, local language delivery, and financing challenges."
AI-assisted heat adaptation is already being tested in a number of cities across the world, from district-level heat mapping systems in India to AI-powered tree canopy planning and cool-roof installations in US cities. However, researchers agree that whether AI can produce climate intelligence is not currently the biggest difficulty.It can do so more and more. The more difficult question is whether Indian cities can develop the institutional coordination, infrastructure, and governance frameworks required to translate that intelligence into effective protection on the ground.