This study investigates predictors of cervical cancer screening uptake among women aged 30–49 in sub-Saharan Africa, where screening rates remain critically low despite the high burden of disease. The authors used a cross-sectional dataset of 33,952 individuals from the 2022 Demographic and Health Surveys in Ghana, Kenya, Mozambique, and Tanzania. Data were preprocessed in STATA and Python, and several machine learning models were trained, including decision trees, gradient boosting, and random forest, with Tomek links and oversampling applied to handle class imbalance. Random forest achieved the strongest performance, with 78% accuracy, an AUC of 86%, and an F1-score of 79%. Detailed feature importance analysis using Shapley Additive Explanations highlighted wealth status, awareness of sexually transmitted infections, HIV testing, age at first sexual intercourse, education level, smartphone ownership, rural vs urban residence, number of sexual partners, and perceived health status as the top predictors of screening uptake. The authors emphasize that the overall screening prevalence across the four countries was just 13%, significantly lower than global averages, and argue that targeted interventions are necessary to close this gap.