Abstract Accurately predicting student adaptability is essential for enhancing academic performance and personal development. The present study presents a classification-based approach utilizing Quadratic Discriminant Analysis (QDA), Extreme Gradient Boosting (XGBoost), and Linear Discriminant Analysis (LDA) to predict student adaptability. To enhance model performance, the Turbulent Flow of Water-based Optimization (TFWO) and Giant Armadillo Optimization (GAO) algorithms are employed.