Due to delayed diagnosis, few treatment choices, and inadequate healthcare infrastructure, breast cancer continues to be a major worldwide health concern, with some of the greatest fatality rates occurring in Sub-Saharan Africa. By improving the treatment of breast cancer, machine learning (ML) and algorithmic technologies provide a game-changing chance to solve these issues. This study examines the current state of breast cancer in Sub-Saharan Africa, emphasizing the disease's startling prevalence and death rates as well as the structural and systemic obstacles to quality care, such as a lack of diagnostic resources, restricted access to treatment, and socioeconomic considerations. ML has a lot of potential in the medical field, especially in the areas of breast cancer early detection, diagnosis, and therapy planning. Medical image analysis and patient prediction have shown the effectiveness of machine learning models, including supervised, unsupervised, and reinforcement learning methods. However, implementing these technologies in Sub-Saharan Africa requires overcoming several barriers, including poor data availability, limited infrastructure, and a shortage of trained professionals. This paper highlights the importance of partnerships between governments, international organizations, and the tech industry in bridging these gaps. Recommendations include improving healthcare infrastructure, training healthcare workers, and developing region-specific ML models using local datasets to ensure cultural and contextual relevance. Addressing ethical concerns, such as data privacy and equitable access, is also emphasized to ensure the sustainable and inclusive adoption of these technologies. By leveraging ML and algorithmic technologies, Sub-Saharan Africa has the potential to significantly improve breast cancer outcomes, reduce mortality rates, and build a more robust and equitable healthcare system. Continued research, investment, and collaboration will be pivotal in achieving this vision.