<p>Traditional knowledge (TK) encompasses centuries of empirical wisdom, practices, and cultural heritage rooted in indigenous communities. However, it faces significant challenges in interpretation, validation, and integration with modern scientific paradigms due to its oral transmission, contextual nature, and epistemological differences from Western science. This paper critically analyzes these challenges, including the loss of knowledge due to modernization, language barriers, documentation issues, and intellectual property concerns. It explores how Artificial Intelligence (AI) can serve as a bridge between TK and contemporary science by enabling knowledge digitization, pattern recognition, multilingual analysis, and contextual understanding. Through examples such as AI-driven ethnobotanical databases, natural language processing for regional dialects, and machine learning for medicinal plant validation, the paper illustrates the transformative potential of AI. It also emphasizes the ethical considerations and risks of cultural misappropriation, data misuse, and algorithmic bias. The study concludes that responsible, inclusive, and culturally sensitive AI can not only preserve but also elevate the role of TK in addressing global challenges like climate change, sustainable agriculture, and healthcare.</p> Graphical Abstract <p></p>