Nature-Based Solutions have emerged as vital strategies for enhancing climate resilience in India’s vulnerable river deltas, yet their long-term effectiveness remains poorly monitored and inconsistently scaled. This chapter develops a comprehensive framework that integrates remote sensing and artificial intelligence to monitor, evaluate, and support the adaptive management of NbS in deltaic contexts. Drawing upon case studies from the Sundarbans, Mahanadi, and Krishna-Godavari deltas, we examine the spatial dynamics of interventions such as mangrove restoration, wetland reconnection, and agroecological zoning. The chapter synthesizes how remote sensing technologies including multispectral, radar, and drone-based platforms, combined with machine learning models such as Random Forest and convolutional neural networks, can quantify vegetation recovery, hydrological shifts, and flood buffering in near real-time. We also analyze key implementation gaps based on national compendiums and field data, particularly around post-implementation monitoring, data silos, and institutional capacity. The proposed framework bridges this gap by enabling AI-enabled, high-resolution NbS monitoring pipelines tailored to Indian governance systems. The chapter concludes with a set of policy-relevant recommendations for embedding such systems into national and subnational climate adaptation plans, with direct relevance to Sustainable Development Goals, the Sendai Framework, and India’s coastal resilience strategy.