This study, presented by Bahati and Masabo from the African Center of Excellence in Data Science (ACE-DS) at the University of Rwanda, broadly investigates the use of machine learning techniques, specifically random forest prediction models, k-means clustering, and linear programming, to determine the most effective placement of ambulance stations across Rwanda. This stems from a need to minimize emergency response times and improve outcomes in regions of Rwanda with high frequencies of road accidents. The researchers used secondary data on emergency responses and road accidents from the Rwanda Biomedical Centre during the fiscal years of 2021-2022 and 2022-2023 and integrated it with the administrative boundaries of Rwandan sectors. An exploratory data analysis was performed to identify high-density accident regions, and a trained random forest model predicted whether response times would be classified as “fast” or “slow,” achieving a reliable accuracy of about 93%. The researchers found that ambulance response times in Rwanda were significantly affected by population density, road accident frequency, and geographic location, with remote rural areas experiencing slower responses. Using k-means clustering and linear programming, they identified 58 optimal ambulance station sites that would reduce the average distance to accident locations by 31.5%, suggesting substantial improvements in emergency coverage. Overall, their findings indicate that data-driven ambulance placement could significantly enhance emergency response efficiency and save more lives in regions with high accident rates. The authors emphasized that machine learning-driven optimization of ambulance locations can significantly reduce disparities in emergency responses between urban and rural areas, ultimately improving equity in access to care across Rwanda. They also highlight that integrating accident data with spatial analysis provides a scalable, evidence-based framework for health infrastructure planning in low- and middle-income countries.