Hypertension is a serious medical condition and a leading cause of death worldwide. The development of accurate predictive methods for Angiotensin Converting Enzyme (ACE) Inhibitors is crucial in the treatment of hypertension. This study aims to develop a predictive model using Camel Algorithm for feature selection and Support Vector Machine (SVM) for building the predictive model with three types of kernels including Radial Basis Function (RBF), SVM-Linear, and Polynomial. The dataset used consists of 255 compounds with ACE inhibitory activity in Rattus norvegicus rats, sourced from the ChEMBL database, structured in SMILES notation, and then converted into SDF format. Experimental results show that the RBF kernel model provides the best performance, with an R² value of 0.8728 on the training data and 0.5620 on the testing data. This study highlights the effectiveness of the Camel Algorithm-Support Vector Machine combination in developing the ACE Inhibitor prediction methodology, and provides a signific