The types of labor are some of the methods chosen by the expectant mother who performs the delivery as well as by the health personnel who handle it. There are two methods of delivery, namely vaginal delivery known as natural delivery and cesarean delivery. In this study, the classification of the type of caesarean delivery was performed using the K-Nearest Neighbor algorithm to determine whether the type of delivery would be given a planned caesarean section or an emergency caesarean section. The aim of this study was to classify the types of delivery in pregnant women useful for providing care information in pregnant women at risk for delivering the baby by caesarean section, planning the timing of delivery and reducing the risk of injury to pregnant women at the time of delivery. The data used for this study is a dataset of pregnant women obtained from UCI Machine Learning repository in the form of data type of delivery. This dataset has 80 records with 6 variables: age (age), delivery number (number of medical personnel involved), delivery time (delivery time), blood of pressure (blood pressure), heart problem (heart problem), and Caesar as label. The data was divided into 64 training data and 16 test data, further the classification process with a value of k = 3 using the KNN algorithm obtained results of accuracy 81.25%, precision 100%, recall 62.5% and F1-score 77%. Results from testing the KNN algorithm yielded good accuracy for predicting the type of delivery of the expectant mother.