The researcher was working on the idea that machine learning, a branch of artificial intelligence has proved more accurate than humans in predicting heart diseases. Congenital Heart Diseases (CHDs) are responsible for a greater percentage of infant mortality worldwide, hence the need to detect and predict the diseases at birth for the administration of proper on time treatment. The target of the project was to come up with an algorithm that can be linked to a device(s) for inputs or that can receive inputs entered manually and give a prediction of potential CHDs. This has been previously addressed as the prediction of heart disease in general but there has not been enough focus on Congenital Heart Diseases hence the need to do the project. The researcher made use of the K-Nearest Neighbours (K-NN), Logistic Regression and Support Vector Machine (SVM) to develop three prediction algorithms in Python Programming. The accuracy of each of the three in prediction was compare the one with the highest score was selected. According to the selected SVM algorithm the researcher saw 73% accuracy in prediction. One recommendation is that more specific data on CHD should be collected to create better datasets.