Traditional ML in drug repositioning often involves utilising illness manifestations, drug targets, and genes as features. This is achieved by establishing a dichotomous network of links between features and medications, where similar disease targets are observed for similar drug actions ( ). In the field of drug repurposing, various machinelearning methodologies have been employed, such as Linear regression and logistic regression ( ), Support Vector Machines (SVMs) ( ), Random Forest (RF) ( ), and deep learning models ( ). The published studies prove that utilising ML drug repurposing presents a potent approach to discovering novel therapeutic applications for existing pharmaceuticals, thus expediting the drug development procedure.