Abstract
<jats:p>This chapter presents a comprehensive artificial intelligence (AI) framework for optimizing climate-resilient infrastructure through integration with nature-based solutions (NbS). The methodology combines predictive analytics, multi-objective optimization, and digital twin technologies to address complex challenges in infrastructure design under climate uncertainty. Through a detailed coastal city case study, the framework demonstrates 55% reduction in flood risk, 30% lower embodied carbon, and 25% cost savings compared to conventional approaches. The chapter provides practitioners with scalable tools for implementing AI-driven resilience strategies while addressing ethical considerations in algorithmic decision-making. Key innovations include hybrid physics-AI modeling, adaptive reinforcement learning systems, and explainable AI interfaces for infrastructure planning.</jats:p>