<p>Machine learning (ML) and Artificial Intelligence (AI) technology have the potential to transform African healthcare delivery systems by optimizing and enhancing areas such as, disease diagnosis, infectious disease surveillance, and resource management and allocation towards infrastructure and personnel. However, the adoption of such technology into the healthcare systems is hindered by significant barriers such as, insufficient digital infrastructure, restricted access to quality health data, low machine learning literacy among healthcare personnel, and ethical concerns about data privacy. Additionally, regulatory uncertainty and under-investment in technology also serve as hurdles to machine learning adoption. Despite these limitations and challenges, possibilities exist in the form of mobile health platforms, increasing internet coverage, and growing improvements and innovation in the digital healthcare space. Machine learning can aid in improving the efficiency of healthcare systems across the continent through strategic partnerships, capacity building, and context-based methods. This study addresses present limitations and challenges, including potential prospects, providing insights into the long-term integration of machine learning in African healthcare.</p>