Researcherinterestineducationdata mining has increased significantly in a variety of sectors. The recent research works use a variety of machinelearningtechniquestopredictstudents' academic success in the educational sectors. They sufferfromseriousdrawbackslikelowforecast accuracy,highprocessingtimes,andoverhead. Therefore, the proposed work aims to develop a new model for projecting students' academic progress. The main goal of this paper is to develop a smart and automatedsystemforpredictingthestudents’academic performance from the given students’data. For this purpose, a novel optimization and deep learningclassificationmethodologiesare implemented in this study. Here, the public UCI education training dataset is obtained to develop the predictionframeworkforforecastingstudents' academic achievement. The most correlated features from the preprocessed schooling dataset are chosen using the War Strategy Optimization (WStO) method to improve predicting performance.