Ectopic pregnancy is an acute health issue that requires urgent attention and precise recognition to ensure timely treatment. The machine learning (ML) classifiers used for medical diagnosis have been gaining popularity and have shown potential in detecting ectopic pregnancy. This study attempts to predict the rate of accuracy and performance prediction of distinct machine learning classifiers for ectopic pregnancy detection. In this study, the performance output of four different algorithms, including random forests (RF), K-Nearest Neighbors (KNN), support vector machines (SVM), and decision trees are compared. The analysis is done by considering a dataset of 298 women's clinical risk factors, signs and symptoms, and serum human chorionic gonadotropin findings. Results show that the SVM classifier has significantly better prediction findings than the other machine learning algorithms. Further evaluation of the SVM classifier was performed by varying the training data and test data. It is observed that among the classifiers, SVM achieved the highest accuracy, outperforming other models with an accuracy of 81%. This was followed by Random Forest (72%), Decision Tree (75%), and KNN (71%). These findings highlight the effectiveness of various machine learning algorithms in detection of ectopic pregnancy and imply that they might be used in clinical practice as an extra diagnostic instrument in addition to traditional techniques.