The purpose of this study is to conduct an empirical investigation and comparison of the effectiveness of various classifiers and ensembles of classifiers in predicting academic performance. The study will evaluate the performance and efficiency of ensemble techniques that employ several classifiers against the performance and efficiency of a single classifier. Reducing student attrition is a serious concern for educational institutions worldwide. Educators are looking for strategies to boost student retention and graduation rates. This is only achievable if at-risk students are appropriately recognized early on. However, most commonly used predictive models are inefficient and inaccurate due to intrinsic classifier limitations and the usage of minor factors. The study contributes to the body of knowledge by proposing the development of optimized ensemble learning model that can be used for improving academic performance prediction. Overall, the findings demonstrate that the approach of employing optimized ensemble learning (OEL) model approaches is extremely efficient and accurate in terms of predicting student performance and aiding in the identification of students who are in the fear of attrition.