Climate change has intensified the frequency, magnitude, and unpredictability of extreme weather events, placing unprecedented stress on infrastructure systems, urban settlements, and governance mechanisms worldwide. As climate risks increasingly intersect with rapid urbanization, socioeconomic inequality, and technological transformation, the need for adaptive, forward-looking, and intelligence-driven infrastructure design has become a central concern in both scholarly and policy-oriented debates. This research article advances a comprehensive theoretical and analytical exploration of artificial intelligence–enabled climate-resilient design, situating it within global climate governance frameworks, technological foresight methodologies, and institutional capacity-building processes. Drawing strictly on the provided body of literature, the study develops an integrated conceptual model that explains how AI-driven predictive analytics, scenario modeling, and adaptive decision-support systems can transform the planning, design, and governance of infrastructure exposed to climate extremes (Bandela, 2025).

The article positions AI not merely as a technical tool, but as a socio-technical system embedded within regulatory regimes, multilevel governance structures, and normative climate commitments articulated by international organizations such as the United Nations Framework Convention on Climate Change and the Intergovernmental Panel on Climate Change (UN, 1992; IPCC, 2021). Through extensive theoretical elaboration, the study examines how AI-driven climate-resilient infrastructure design aligns with the Sendai Framework for Disaster Risk Reduction, Climate Action Pathways, and emerging national and subnational policy initiatives aimed at enhancing adaptive capacity (Uchiyama et al., 2021; UNFCCC, 2021). Particular attention is given to the role of information technology infrastructure, foresight methodologies, and participatory digital tools in reducing epistemic uncertainty and enabling anticipatory governance in the face of hard-to-predict climate shocks (Shobande et al., 2024; Sytnik & Proskuryakova, 2024).

Methodologically, the article adopts a qualitative, interpretive research design grounded in systematic literature synthesis and comparative policy analysis. Rather than empirical quantification, the study emphasizes deep contextual interpretation of existing scholarly, institutional, and policy-oriented sources to identify patterns, tensions, and opportunities in AI-enabled climate resilience strategies. The results section presents a descriptive analysis of how AI-driven approaches reshape infrastructure risk assessment, urban form, industrial resilience, and climate finance allocation, particularly in climate-vulnerable regions. The discussion critically interrogates ethical risks, institutional constraints, data governance challenges, and uneven technological capacities that may undermine the transformative potential of AI if left unaddressed.

By synthesizing diverse strands of climate resilience scholarship into a unified analytical framework, this article contributes to ongoing debates on adaptive infrastructure governance and digital transformation under climate uncertainty. It concludes by outlining future research pathways that prioritize interdisciplinary integration, equity-oriented design, and the alignment of AI innovation with global climate justice objectives.

Climate resilience, artificial intelligence, infrastructure governance, extreme weather adaptation

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