A Dissertation Submitted in Partial Fulfilment of the Requirements for the Degree of Master’s in Information and Communication Science and Engineering of the Nelson Mandela African Institution of Science and TechnologyMaternal mortality remains a global problem, with approximately 830 women dying every day as a result of childbirth and pregnancy complications. The maternal mortality ratio is as high as 524 deaths per 100 000 live births in Tanzania. The main causes of maternal mortality in developing countries are all linked to poor prenatal care, which is partially caused by treatment delays. Studies show that providing women with maternal health information can help achieve the goal of reducing global maternal mortality to less than 70 maternal deaths per 100 000 live births by 2030. Through Natural Language Processing (NLP), we leveraged the use of BERT question and answer model which is a pre-trained model that wasfine-tuned to develop a model that can diagnose pregnancy complications, explain possible causes in simple language, and provide recommendations for care and treatment, for Malaria, Pregnancy hypertension (Pre eclampsia), and miscarriage (Threatened abortion) which are the main preventable causes of maternal mortality in Tanzania. The expert system is embedded in a maternal smartphone app, MamaApp, that provides weekly information on fetal development, regular and concerning pregnancy symptoms, and self-care tips. The expert system model was able to diagnose the three conditions with confidence ranging from 79% to 100%. Validation of MamaApp in Arusha showed high acceptance from both the expectant mothers and doctors.