<p>The growing use of artificial intelligence (AI) in environmental governance is transforming how climate risks are monitored, modeled, and managed. However, most AI-based systems remain grounded in Western epistemological frameworks, frequently overlooking Indigenous knowledge systems (IKS) that provide place-based, relational, and long-term understandings of ecological change. This article examines the opportunities and challenges of integrating Indigenous knowledge into AI-driven approaches to climate resilience. Using an interdisciplinary qualitative methodology that combines critical literature review, comparative case analysis, and environmental justice theory, the study analyzes documented initiatives from Indigenous territories in the Global South. These cases illustrate how automated tools—such as climate prediction models, resource monitoring platforms, and conservation algorithms—can either reinforce data extractivism and epistemic marginalization or contribute to more inclusive and context-sensitive forms of environmental governance. The article argues that meaningful integration of Indigenous knowledge into AI systems requires more than technical adaptation. It demands ethical governance frameworks that recognize epistemic plurality, community consent, and Indigenous data sovereignty. By critically assessing both risks and enabling conditions, this study contributes to emerging debates on decolonial AI and highlights pathways through which AI-based environmental governance can support climate resilience while respecting Indigenous rights and knowledge systems.</p>