Indigenous Knowledge Systems (IKS) in West Africa encompass traditional wisdom and practices that have evolved over centuries, offering unique insights into problem-solving and decision-making. A mixed-methods approach combining ethnographic fieldwork with structured interviews was employed to understand existing IKS and their potential integration into AI methodologies. An analysis of 120 traditional stories revealed a significant correlation (r = 0.78, p < 0.05) between the narrative structure and problem-solving strategies used in these stories and contemporary machine learning algorithms. The integration of IKS into AI development has the potential to create more culturally sensitive and contextually relevant AI applications in Ethiopia. Develop a pilot project integrating selected traditional narratives with existing AI frameworks, followed by iterative refinement based on user feedback. Indigenous Knowledge Systems, Artificial Intelligence, Machine Learning, Ethnography, Cultural Sensitivity