Globally, the acceptance and use of herbal and traditional medicine is on the rise. Africa, especially Ghana, has its populace resorting to African Traditional Herbal Medicine (ATHMed) for their healthcare needs due to its potency and accessibility. However, the practice involving its preparation and administration has come into question. Even more daunting is the poor and inadequate documentation covering the preservation and retrieval of knowledge on ATHMed for long-term use, resulting in invaluable healthcare knowledge being lost. Consequently, there is the need to adopt strategies to help curtail the loss of such healthcare knowledge, for the benefit of ATHMed stakeholders in healthcare delivery, industry and academia. This paper proposes a hybrid-based computational knowledge framework for the preservation and retrieval of traditional herbal medicine. By the hybrid approach, the framework proposes the use of machine learning and ontology-based techniques. While reviewing literature to reflect the existing challenges, this paper discusses current technologies suited to approach them. This results in a framework that embodies an ontology driven knowledge-based system operating on a semantically annotated corpus that delivers a contextual search pattern, geared towards a formalized, explicit preservation and retrieval mechanism for safeguarding ATHMed knowledge.