Artificial intelligence is emerging as a transformative tool in antimicrobial resistance surveillance across Africa. A consortium comprising the University of Cape Town, KEMRI in Kenya, the University of Ibadan in Nigeria, and the Institut Pasteur de Madagascar has developed and validated machine learning models capable of predicting antibiotic resistance profiles with an unprecedented accuracy of 89%. The models, named AfriAMR-Predict, utilize deep neural networks trained on a database of over 2 million antibiograms collected from 200 African hospitals between 2018 and 2025. By integrating clinical variables (age, diagnosis, hospitalization history), geographic variables (region, urban/rural setting), and temporal variables (season, epidemiological trends), the models can recommend the most appropriate antibiotic treatment before culture results are even available. Professor Tulio de Oliveira, bioinformatician at the University of Cape Town and lead architect of the project, explained that "on a continent where fewer than 10% of antibiotic prescriptions are guided by an antibiogram, AI can substantially bridge this diagnostic gap." He added that the models are designed to run on smartphones with limited internet connectivity. A pilot study conducted in 20 Kenyan hospitals showed that using AfriAMR-Predict reduced inappropriate antibiotic prescriptions by 22% and improved clinical outcomes for patients with severe infections by 18%. The consortium plans to deploy the tool across 500 healthcare facilities in 15 countries by the end of 2027.