<p>Northeast India is a cradle of cultural and ecological diversity, where Indigenous communities have cultivated rich traditional knowledge systems across generations—ranging from sustainable agroforestry, medicinal plant use, and sacred landscape management to complex oral histories and language traditions. However, these knowledge systems now face accelerating threats from environmental degradation, climate change, socio-economic shifts, and cultural erosion. This review explores how artificial intelligence (AI) and machine learning (ML) can serve as transformative tools for documenting, preserving, and revitalizing Indigenous knowledge in ways that are ethically grounded and community-driven. We present a comprehensive framework that integrates diverse AI capabilities—including natural language processing for endangered dialects, computer vision for ecological monitoring, geospatial modeling of traditional land-use, and predictive analytics for resilience planning. Emphasizing participatory design, we advocate for AI systems co-developed with Indigenous stakeholders, respecting data sovereignty and guided by the CARE principles—Collective benefit, Authority to control, Responsibility, and Ethics. Through inclusive workflows that build local capacity and align with Indigenous values, AI can enhance both scientific understanding and cultural continuity. The review also addresses critical challenges such as algorithmic bias, digital infrastructure gaps, and intellectual property protections, proposing hybrid governance models that combine traditional leadership with digital innovation. By bridging advanced computational tools with Indigenous stewardship, this approach offers a path toward the long-term resilience, autonomy, and flourishing of Northeast India’s Indigenous Knowledge Systems.</p>