Web applications that employ SQL datasets are at serious risk from SQL injection attacks. Using two separate datasets, this study examined the utility of ML classification approaches for identifying SQL injection threats. On two datasets—one taken from the actual world and the other created artificially—random forest (RF), descion tree (DT), k-nearest neighbours (KNN), gradient boost (GB), xgb classifier, linear SVM (Support Vector Machines), and RBF (Radial Basis Function) SVM were trained. The outcomes demonstrated that both algorithms could precisely and precisely detect SQL injection attacks. The model that was trained using the synthetic dataset outperformed the model that was trained using the realworld dataset. These findings highlight the potential of ML classification for identifying SQL injection attacks as well as the value of employing a variety of datasets to increase model accuracy (ACC).