Hybrid Ontology and Machine Learning Approaches in Developing Knowledge-Based Systems for African Traditional Medicine: A Literature-Based Review

African Traditional Medicine (ATM) has long served as a primary healthcare system across the continent, yet its integration with modern digital health frameworks remains limited. This paper presents a systematic review of 20 recent peer-reviewed studies focused on hybrid knowledge-based systems (KBS) that combine ontology and machine learning (ML) techniques for digitizing and supporting ATM practices. The review highlights key advances, including ontology-driven knowledge representation, semantic interoperability, and AI-enhanced diagnostic support. Findings show that while hybrid approaches improve knowledge preservation, diagnostic accuracy, and explainability, most systems remain locally scoped, culturally specific, and under-tested in real-world contexts. Identified gaps include limited cross-cultural generalization, inadequate use of adaptive ML methods, weak cross-lingual support, and lack of large-scale clinical validation. To address these challenges, the study proposes a scalable, multilingual, and ontology-driven framework enhanced with ML for adaptive diagnosis and decision support. This work underscores the transformative potential of hybrid AI systems in preserving indigenous medical knowledge, advancing healthcare accessibility, and promoting culturally inclusive digital health innovation across Africa.