The aim of this study was to determine, using Machine Learning (ML) algorithms, whether a pregnant woman will give birth by caesarean or not. The study is based on quantitative analysis using secondary data from the obstetric department of the Wanamahika Hospital in the city of Butembo, the Democratic Republic of Congo, over a period of one year and seven months in 2019 and 2020. The used dataset comprises 1501 records. Six ML models, namely: the Decision Tree, the Support Vector Machines, the Artificial Neural Networks, the k-Nearest Neighbors, the Random Forest as well as the Logistic Regression (LR), were built to predict the mode of women deliveries. The evaluation metrics used to evaluate those models were accuracy, f1-score, precision and recall. The authors found that cesarean deliveries represented 33.8% of their study sample while vaginal deliveries represented 66.2%. Of those six ML models created, LR was validated as it performed better with an accuracy reaching 98.85%, a recall, a precision, and a f1-score of 0.99, respectively. At the end of the investigation, the researchers retained LR in order to deploy a Web application that detect the mode of women deliveries in the hospital using Flask. Nineteen features revealed to be predictors of delivering in caesarean mode in the City of Butembo that are Referred by the health center, Age of the mother, Origin of the mother, Age of the pregnancy, HIV test result, Antepartum hemorrhage, Uterine rupture, Eclampsia and pre-eclampsia, Active management of the third period of labor, Indications for cesarean section, Number of previous cesarean sections, Episiotomy, Theobald, Cytotec, Sex of live birth, Obstetric formula, Weight of newborn in grams, Number of days in hospital, Number of days before delivery.