Artificial Intelligence–Enabled Climate-Resilient Infrastructure Design: Integrating Predictive Adaptation, Governance, and Sustainability for Extreme Weather Futures

Abstract

The accelerating frequency and intensity of extreme weather events have fundamentally altered the risk landscape for infrastructure systems worldwide, exposing deep structural, governance, and planning vulnerabilities. Traditional approaches to infrastructure design, which rely heavily on historical climate data, deterministic safety margins, and linear planning assumptions, are increasingly inadequate under conditions of climate volatility and systemic uncertainty. In response, artificial intelligence has emerged as a transformative enabler of climate-resilient infrastructure, offering advanced predictive, adaptive, and decision-support capabilities that can fundamentally reshape how infrastructure is conceived, designed, managed, and governed. This research article develops a comprehensive, theory-driven examination of AI-enabled climate-resilient infrastructure design, synthesizing insights from climate science, infrastructure economics, development policy, human rights discourse, and emerging AI-driven design paradigms.

Anchored in contemporary scholarship on climate-smart and resilient infrastructure, this study critically engages with AI-driven predictive and adaptive frameworks that integrate real-time data, machine learning models, and scenario-based simulations to anticipate extreme weather impacts and inform dynamic infrastructure responses. Particular attention is given to the conceptual and practical contributions of recent research on AI-driven climate-resilient design, which emphasizes predictive adaptation and continuous learning as core principles for infrastructure sustainability under climate stress (Bandela, 2025). Building on this foundation, the article situates AI within broader debates on sustainable development, low-carbon transitions, and equitable infrastructure provision, drawing extensively on multilateral development, policy, and governance literature.

Methodologically, the study adopts an integrative qualitative research design based on systematic literature synthesis, comparative case interpretation, and theoretical triangulation across sectors including energy, water, transport, and health infrastructure. The results demonstrate that AI-enabled approaches significantly enhance infrastructure resilience by improving hazard prediction, optimizing design under uncertainty, supporting adaptive operations, and enabling cross-sectoral coordination. However, these benefits are contingent upon governance capacity, data integrity, institutional alignment, and ethical safeguards.

The discussion advances a critical analysis of the tensions between technological optimism and socio-political realities, highlighting risks related to data bias, digital divides, human rights impacts, and the marginalization of vulnerable communities. The article argues that AI-driven climate-resilient infrastructure must be embedded within robust governance frameworks, rights-based approaches, and sustainability narratives to avoid reproducing existing inequities. Ultimately, the study contributes an original, interdisciplinary framework for understanding AI as both a technical and socio-institutional catalyst for climate-resilient infrastructure, offering policy-relevant insights for governments, development institutions, and researchers navigating the infrastructure challenges of a climate-disrupted future.