Advancing climate resilience through artificial intelligence: A comprehensive review across urban and climate-sensitive sectors in Morocco

Climate change poses significant challenges to Morocco, which is increasingly affected by desertification, recurrent droughts, rising temperatures, and water scarcity. These impacts threaten climate-sensitive sectors such as agriculture, water resources, energy, urban planning, and environmental management, highlighting the urgent need for innovative and data-driven resilience strategies. In this context, artificial intelligence has emerged as a promising tool for strengthening prediction, planning, risk management, adaptation, and mitigation. This review assesses how artificial intelligence, machine learning, and deep learning have been applied to climate resilience in Morocco across seven climate-sensitive sectors and identifies methodological, data, and deployment gaps. A structured search of Google Scholar, IEEE Xplore, ScienceDirect, and SpringerLink identified 80 eligible publications from 2020–2025, which were analyzed by sector, model family, dataset and access type, geographic scale, evaluation metric, and stated or implicit research gap. The findings show a marked increase in research activity after 2022, concentrated particularly in agriculture, water resources, and climate-risk applications. Tree-based ensembles were prevalent for heterogeneous tabular and geospatial data, whereas neural models were used mainly for imagery and time-series forecasting. Public satellite and reanalysis products enabled broad coverage, but sparse ground observations, restricted institutional data, resolution mismatch, limited spatial transfer testing, and minimal use of explainable AI constrained reproducibility and operational uptake. Most applications supported adaptation, while mitigation remained less developed. By jointly connecting AI methods, data provenance, spatiotemporal integration, interpretability, sector-specific limitations, and policy readiness, this Morocco-specific cross-sector synthesis extends narrower domain reviews and provides an evidence-based roadmap for reproducible, locally generalizable, and trustworthy climate-AI systems.