###### Abstract
Anthropometric measures are non-invasive quantitative measurements of the body that include height, weight, head circumference, Body Mass Index (BMI), body circumferences (waist, hip, and limbs) to determine adiposity, and skinfold thickness [8]. There is a growing body of evidence on the use of anthropometric measures to predict hypertension in children and adolescents, some of which may be found in [4,9,10,11,12]. The commonly used anthropometric measurements for hypertension prediction include body mass index (BMI), waist circumference (WC), weight-to-hip ratio (WHR), and weight-to-height ratio (WHtR). Nonetheless, data suggest that the predictive abilities of anthropometric measurements for hypertension vary by country and ethnicity [13]. As machine learning (ML) has gained traction in the medical field, new algorithms for predicting hypertension have emerged. When it comes to hypertension, machine learning technologies might be used as a supplementary tool or a second opinion to assist medical doctors in making timely decisions. The use of anthropometric measurements in ML models yielded varied results with different models. The following review focuses on some of the latest research that employed multiple ML models to predict hypertension using anthropometric measures as the input features. [PERSON] et al. [14] utilized a dataset of 29,700 participants aged 18 to 70 years old to deploy four ML models, namely Random Forest (RF), CatBoost, Multi-layer Perceptron (MLP) neural network, and Logistic Regression (LR) for hypertension risk prediction. Along with anthropometric measures, their work utilized demographic and lifestyle data as inputs to the machine learning algorithms. The ten selected input features were age, gender, BMI, WC, family history, occupation, smoke, drink, healthy diet, and physical activity. The data were randomly divided into training and validation in the ratio of 4:1. During the training stage, the training set was divided into 9:1 for training and verification sets. On the test set, the models' performance was measured by the Area Under Curve (AUC), accuracy, sensitivity, and specificity. They concluded that RF performed the best with AUC = 0.92, accuracy = 0.82, sensitivity = 0.83, and specificity = 0.81. In another research by [PERSON] et al. [15], ML models were used to develop risk stratification algorithms for diabetes and hypertension. Five ML models, including Decision Tree, regularized Logistic Regression, k-Nearest Neighbor, RF, and AdaBoost, were developed and tested in their study. The input of the models included data from the questionnaire: weight, height, waist circumference, blood pressure, heart rate, and blood glucose, and the output for hypertension classification was based on the assessment of the medical doctor. Using AUC to measure the performance of the models, they discovered that RF (0.792) performed slightly better than Logistic Regression (0.776), followed by AdaBoost (0.770), k-Nearest Neighbor (0.705), and Decision Tree (0.610). The sample size employed in the study was 2278, with an average age of 50.6. A three hidden layers Artificial Neural Network (ANN) model was developed as a classification model for hypertension patients using gender, race, BMI, age, smoking, kidney disease, and diabetes in [16]. Using an imbalanced dataset of 24,434, with 69.71% non-hypertensive patients and 30.29% hypertensive patients, the model was compared with decision forest, Logistic Regression, Support Vector Machine, boosted Decision Tree and Bayes point machine. The ANN model developed managed to achieve a sensitivity of 40%, a specificity of 87%, precision of 57.8%, and a measured AUC of 0.77. They concluded that the accuracy of the approaches is relatively similar when compared to the other five ML models, but that the AUC and _F1_-score of the ANN method are somewhat higher and more competitive. As evidenced by the review, various ML models were developed and employed for hypertension prediction.