A conceptual framework of the application of AI to develop and implement policies to predict, prevent, and mitigate the consequences of climate migration in an equitable manner is illustrated in Fig. 1. Drawing upon the experience of the application of AI tools to date in the fields of health care and public health, climate and weather forecasting, and migration prediction and management, we posit that these tools may promote climate migrant equity by enhancing decision-making capacity to assist in pre-migration prediction and prevention, thereby reducing disparities in the risk of displacement in vulnerable communities, and post-migration mitigation of the social, physical and psychological consequences of displacement experienced by climate migrants.
Conceptual framework of the use of AI for climate migration.
While many AI applications (e.g., ecological monitoring, disaster response systems, and health vulnerability assessments) are not unique to climate migration per se, their deployment becomes highly consequential when applied in contexts where migration is both a response to and a driver of vulnerability. This framing makes clearer how AI tools must be adapted and governed differently when used in displacement-affected regions. We have therefore structured this commentary to show not just generic AI capabilities but their specific relevance to migration-related challenges.
AI does not automatically generate equitable outcomes, but it can reshape decision-making by changing when risks become visible, whose data counts as evidence, and how early interventions are coordinated. Evidence from health and disaster contexts suggests such mechanisms can improve equity and well-being, though whether they do so in climate migration contexts remains an open empirical question.
Disaster preparedness and response
AI-driven systems already aid disaster preparedness by providing early warnings for hurricanes, wildfires, droughts, and other extreme events (Albahri et al., 2024). Google DeepMind and Huawei have developed AI models that outperform conventional weather models in predicting hurricane tracks and weather patterns while using less computation. Google DeepMind’s GenCast, for example, is an ML learning weather prediction method that applies graph neural networks (GNN) to meteorological data (Price et al., 2025). Such predictions are critical to the development of plans to prepare for and respond to disasters like climate-related extreme weather events. However, such systems are not readily available worldwide, especially in low-income countries and small island developing states (Kuglitsch et al., 2024). Countries in the Global South and rural areas report the largest gaps in early warning systems due to limited infrastructure, the digital divide, and warning messages that are not adapted to local languages, knowledge systems, or cultural contexts (Tiggeloven et al., 2025). Moreover, AI’s reliance on high-quality datasets raises concerns about data accessibility and bias, as lower-income regions remain underrepresented in AI-driven planning models (Aczel et al., 2025). While these models are not migration-specific, integrating them into disaster preparedness protocols for at-risk communities offers a concrete pathway for anticipatory relocation planning and humanitarian logistics.
AI-powered ecological monitoring efforts also have a part to play in climate migration prevention. For instance, the International Organization for Migration (IOM) and Microsoft are collaborating to address climate-induced migration by using AI through innovative pilot projects with Microsoft’s AI for Good Lab in the Maldives, Ethiopia, and Libya (IOM, 2024). In Ethiopia, 700,000 people and 1.5% of the country’s croplands were found by this method to be at risk of flooding. This information has enabled IOM to exercise anticipatory action in planning more effective interventions, supporting displaced populations and reducing the need for migration by strengthening resilience to future challenges and the likelihood of future displacement. Data collected by satellites or networks of autonomous sensors can also be analyzed in real-time using deep-learning models, Random Forests (RF), support vector machines, cloud based platforms like Google Cloud AI, and big data analytics tools like Hadoop and Apache Spark to map and predict the spread of pests, changes in soil quality, or rapid degradations in habitat quality (Olawade et al., 2024). Bayesian networks, neural networks, RFs, and decision-trees (DT) have been identified as the most used ML methods to provide timely insights into biodiversity and habitat quality that can help communities to safeguard their food and water security and livelihoods (Mu et al., 2024).
However, there are potential risks associated with the convergence of AI and emergency responses, as AI agents could inadvertently exacerbate the damage sustained by hazard-impacted communities, potentially exceeding the initial hazards themselves. Furthermore, decision-making by these AI agents during emergency response campaigns may unintentionally perpetuate societal inequality across hazard-affected communities. Without vigilant oversight, their actions could unwittingly exacerbate existing disparities (Sun et al., 2024).
Health disparities
AI and big data analytics may help to address public and global health needs and reduce health disparities in low- and middle-income countries (LMICs) most impacted by climate change (Qoseem et al., 2024). These include ML algorithms (TF, deep neural network [DNN], and Gaussian process) using temperature change and carbon emissions data to predict vulnerability to health risks associated with heat waves and air pollution (Côté et al., 2024), collection and analysis of environmental and sociodemographic data to predict disease outbreaks, mitigation or treatment of climate-related mental health burdens prior to, during, and post-migration, and improving access to healthcare for chronic and infectious diseases exacerbated by climate change.
In Bangladesh, ML models such as long short-term memory (LSTM) networks and RF using remote sensing and environmental data have been employed to predict disease outbreaks following floods, with real-time alerts issued to public health authorities (Khan et al., 2025). RF and Convolutional neural networks (CNNs) and mobile phone data have shown promise in predicting diarrheal disease risk in under-5 children in Ethiopia (Zemariam et al., 2024).
AI ML applications like neural networks, DT, and ChatGPT are also being widely used in health services delivery, including medical imaging and diagnostics, virtual patient care, patient engagement and adherence to treatment, and rehabilitation (Al Kuwaiti et al., 2023). Digital health technologies that utilize AI tools such as ML, distributed ledger technology, and natural language processing (NLP) have been recommended for addressing the physical and mental health care needs of migrants and displaced persons (IOM, 2022; Matlin et al., 2025). One example is the Digital REACH Initiative of seven East African countries that created a regional roadmap for digital health to deliver quality health services and public health regulations to migrants and border communities (East African Health Research Commission, 2017). However, additional research is required to determine whether these applications can be scaled and sustained in migrant or displaced populations. To date, there has been limited empirical evidence that AI-driven health interventions have reduced morbidity, improved continuity of care, or narrowed health inequities among displaced populations over time.
Community sustainability
Much of the evidence base for AI-enabled urban sustainability is concentrated in higher-income settings, with more limited applicability to rural and informal contexts where many displaced populations originate and settle (Aczel et al., 2025). AI can be applied to reduce the need to migrate in response to climate change and strengthen the resilience of climate migrant communities of origin by contributing to smart recycling, carbon capture, and geoengineering, and nudge consumers to adopt conservation measures and create more awareness about the environmental and climate impact of their consumption habits (Stern et al., 2025). Large language models (LLM) and digital twins (DT) can enhance communication of climate risks to communities, helping to influence decisions and raise awareness about potential impacts (Reichstein et al., 2025). These tools can also be used to move transportation systems to less carbon emissions and more efficient energy management and routing (car traffic, shipping, etc.), track deforestation and carbon emissions by industry, or improve predictions of energy needs and manage energy consumption (Vinuesa et al., 2020). What remains unresolved is whether these tools can specifically alter migration pressures or outcomes, or can be applied to rural, agrarian, or informal settlements that dominate climate displacement patterns globally.
AI can also be used to promote the social and economic as well as environmental sustainability of host communities. For instance, AI methods such as ML can be integrated into the digitization of the 15-min city approach to reducing the energy demands and carbon footprints of urban areas across the globe (Moreno et al., 2021). The use of AI-driven smart technologies can also facilitate social and economic sustainability by developing mixed-use neighborhoods that bring together climate immigrants and established residents for social and economic gains, leading to increased social coherence (Allam et al., 2022). AI-driven smart city interventions risk exacerbating exclusion where migrants lack legal status, digital access, or political voice. Without safeguards, such systems may reinforce surveillance and service rationing rather than inclusion (Aczel et al., 2025).
Resettlement
While AI and statistical models have been employed to predict extreme weather events, environmental degradation, and health risks that may serve as drivers of climate migration, they can also be employed to predict migration patterns and outcomes, including how many people are moving and where they are moving to (Beduschi and McAuliffe, 2021; Hossain et al., 2023). For instance, Robinson and colleagues (2020) coupled models of sea level rise (SLR) with Artificial Neural Networks (ANN) and eXtreme Gradient Boosting (XGBoost) ML methods for modeling human migration to predict migration patterns in the United States. Aoga and colleagues (2024) applied DT and XGBoost to examine the association between weather shocks and intention to migrate, using data from the Standardized Precipitation-Evapotranspiration Index of six Western African countries and data from the Gallup World Poll between 2009 and 2015. ML models like LSTM and XGBoost may also help predict whether people will choose to migrate or stay in place when faced with climate risks (Hossain et al. 2023). Furthermore, ML approaches may also be used to identify and deliver services for climate migrants in need, including assistance with asylum and visa applications, language translation, employment, health and welfare, education, social integration and housing security (Fan et al., 2018), identify suitable resettlement locations (Bansak et al., 2018), and forecast migrant resource needs to help communities prepare for their arrival (Beduschi and McAuliffe, 2021). For instance, the United Nations High Commissioner for Refugees (UNHCR) has been leveraging LLMs and GenAI models to predict refugee movements and allocate housing, food, and medical aid resources accordingly (UNHCR, 2025). However, globally, AI-assisted allocation systems raise concerns about privacy, security, vulnerability, transparency, migrant preferences, cultural continuity, and resettlement outcomes (Beduschi, 2021; Nalbandian, 2022).
Child development
Climate change and climate-related population displacement pose a threat to three essential requirements for healthy child development: safety, permanency, and well-being (Clark et al., 2020). The application of ML and LLM models in education platforms found to result in improved social, cognitive, and language development in early childhood (Xu et al., 2025) may be tailored to meet the learning needs of vulnerable populations, including climate migrant children (UNESCO, 2022), thereby reducing disparities in development. AI tools such as speech recognition, computer vision, and LLMs have been incorporated into information and communications technology (ICT) such as mobile apps that have demonstrated great potential for facilitating literacy, language learning and self-directed learning for immigrants, refugees, and internally displaced persons (UNESCO, 2022). ML algorithms like CNNs and LLMs like generative pre-trained transformer 4 (GPT-4) have also demonstrated great promise in predicting, preventing, and managing emerging infectious diseases and undernutrition related to climate change in children in LMICs who are at risk for displacement and migration (Torres-Fernandez et al., 2024; Zemariam et al., 2024). AI methods such as ML, deep learning, and NLP techniques also offer promising avenues to promote resilience and mitigate climate-related mental health burdens in children and adolescents prior to, during, and post-migration (Parnes and Weiss, 2025). Although trauma, disrupted caregiving, language loss, and legal precarity often limit the effectiveness of digital interventions for displaced children, several empirical studies have documented the effectiveness of these interventions in reducing symptoms and stigma and improving mental health literacy among displaced refugees in general (Liem et al., 2021) and displaced youth in particular (Raknes et al., 2024). However, there are numerous technical, ethical, equitable, privacy, and data security challenges to AI applications for the development of displaced children that point to the need for more in-depth research on the subject (Campbell et al., 2025; Xu et al., 2025).