This study investigates why cervical cancer screening rates are so low among women in sub-Saharan Africa and whether machine learning can help identify the main factors behind it. The researchers used data from 4 countries and worked with more than 30,000 cases to train their models. They cleaned and scaled the data and handled class imbalance so the models would pick up real patterns. After testing 7 different approaches, the random forest model ended up being the most accurate. The screening rate in the dataset was only 13%, which is noticeably lower than what earlier research suggested. SHAP analysis showed that wealth, STI awareness, HIV testing, and age at first sexual intercourse were some of the strongest predictors of screening. Education level, rural versus urban residence, and smartphone ownership also played important roles. These personal and social factors together shaped who was more likely to get screened. The authors argue that understanding these predictors can help build more effective screening programs in the region.