Accelerating planetary crises are driven by climate change, which contributes to biodiversity loss, tipping points in natural systems and increasing pollution. Building resilient systems across various sectors, urban areas and communities has become a key objective to ensure sustainability and enhance ecosystems. In this context, AI systems for climate resilience are being developed as multidisciplinary tools used by diverse stakeholders to more effectively address the challenges of climate disruption through the formulation of robust climate policies and action plans. The methodological framework of the research is based on a qualitative approach, within which descriptive methods are employed to analyze available academic literature, studies, and developed AI systems for climate resilience. The theoretical framework of the research draws on OECD-identified indirect positive environmental impacts of ICT on ecological sustainability and the UNESCO Recommendation on the Ethics of AI, which stipulates that AI systems should not be used when there are disproportionate negative environmental impacts. Through a synthesized and holistic approach, the research offers a framework and recommendations for a future AI model as a potential response to the triple planetary crisis encompassing climate change, biodiversity loss, and pollution. Accordingly, the research objectives relate to the analysis of AI systems for climate resilience (TerraScope and Climate Trace): practical implementation examples, contribution to building climate resilience, analysis of the sectors in which they are applied (agriculture, infrastructure and other sectors), and contribution to the formulation of climate-resilient policies. The study’s findings point to potential gaps in the use of AI systems for climate resilience, as well as, the need to strengthen their positive effects and contributions—particularly in the context of data management to enhance climate policy-making: data-driven policy development, crisis management, planning climate-resilient urban infrastructure, and other decision-making processes.