A Project Report Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science in Embedded and Mobile Systems of the Nelson Mandela African Institution of Science and TechnologyThe initiation of Dolutegravir based antiretroviral therapy has provided a potent treatment option for persons living with human immunodeficiency Virus (PLHIV). However, clinical research has shown overwhelming evidence that use Dolutegravir (DTG) results into consequential hyperglycaemia. The incidence and prevalence rates of Dolutegravir associated hyperglycaemia among PLHIV are unknown. Therefore, identification of patients susceptible to dolutegravir associated hyperglycaemia is critical to lessening morbidity and mortality associated with uncontrolled high blood glucose level among patients on antiretroviral therapy (ART) and care. The current procedures practiced in screening for hyperglycemia among PLHIV being switched to DTG and DTG Associated Hyperglycemia related literature were appraised. Various machine learning classification algorithms were employed to come up with the most appropriate model. The purpose of the study was to develop an efficient DTG associated hyperglycaemia Screening tool for treatment experienced PLHIV being switched to DTG in Uganda. The study found Extreme Gradient Boost Classification model as the best with performance and evaluation metrics as follows: model accuracy is 0.99, probability to classify positives is 0.87, precision probability for predicting positives is 0.67, Area under the receiver operating Characteristic curve is 0.90, Area under a precision, recall curve as 0.86, F1 score is 0.76, and Cohen Kappa score as 0.72 inter alia. Therefore, the study recommends the adoption and use of the DTG associated hyperglycaemia screening tool while switching HIV treatment experienced patients to a DTG based regimen. This is because the world over is using Machine learning tools to support Medicare. The researcher also suggests stratifying the data by gender and build DTG associated prediction models for women and men separately because men are not affected in any by variables of pregnancy and post-menopausal phase like women. The research proses lessening of bureaucratic tendencies and high charges levied on research protocols and data acquisition as a way of promoting these innovative studies among others.