Indigenous languages of Latin America have faced significant decline due to colonization, globalization, and sociopolitical factors. While some languages remain endangered, others have entirely disappeared, leaving behind limited historical records or, in some cases, none at all. This study explores the historical transmission of these languages, the current state of documentation, and the role of artificial intelligence (AI) in their recovery, including revitalization and reclamation. Focusing on endangered languages such as Bribri, Cabécar, Maléku, Ngäbere, and Kuna, alongside extinct languages such as Muisca, Cumanagoto, Lenca, Charrúa, and Puelche, this research examines how AI-driven natural language processing (NLP), Optical Character Recognition (OCR), and Text-to-Speech (TTS) synthesis contribute to Indigenous language reconstruction and learning. Furthermore, the study explores the emerging role of text-to-video AI technologies, which can generate immersive audiovisual learning materials to facilitate oral language transmission, contextualize linguistic structures, and support culturally embedded storytelling practices. In employing a qualitative historical analysis combined with digital linguistics, this research highlights AI’s potential to bridge critical language gaps, develop culturally relevant teaching materials, and enhance Indigenous-led language recovery initiatives in Latin America.