Background: Ectodermal dysplasia (ED) is a monogenic genetic rare disorder characterized by primary defects in two or more structures derived from the ectoderm, namely hair, nails, teeth, and sweat glands. These patients have a wide diversity of characteristic phenotypic features that range from mild to severe, consequently affecting their quality of life to varying degrees. To our knowledge, no study has compared the performance of different machine learning algorithms in predicting the phenotype severity of ED cases from their genotype profiles. Accurate prediction is vital for future prognoses and the early planning for future therapies and proper preparation for genetic counseling for parents and patients.\nMethods: We applied six different modeling techniques, (Decision Trees (DT), Random Forests (RF), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and penalized Logistic Regression (LR)) to a total of 41 ED patients with 569,650 single nucleotide polymorphisms (SNPs), and compared their performances in phenotype prediction. We aimed to optimize the receiver operating characteristics area under the curve (ROC-AUC) as our primary metric of interest in model evaluation and algorithm choice. \nResults: Ensemble techniques (RF and XGboost) together with KNN recorded comparable and the highest ROC-AUC values of 0.9 with a 95% confidence interval (0.723-0.9999), while penalized LR gave much lesser prediction ability to distinguish between different classes (ROC-AUC (95% CI)) = 0.6 (0.31 – 0.89). \nConclusion: We can predict the phenotype of ED patients with a respectable level of accuracy and that will help them improve their quality of life. Small-sized datasets that are obtained from local studies should not hamper researchers from implementing ML algorithms for prognostication purposes. The privilege of using ML in prognostic modeling may be dependent on multiple factors like sample size, selected features, and the underlying disease of interest.