ABSTRACT: Keeping track of early indications regarding students’ progress helps academics optimize their learning tactics and focus on varying educational practices to make the learning experience successful. Machine learning applications can help academics to predict the expected weaknesses in learning processes and as a result, they can proactively engage such students in better learning experiences. This paper examines the effectiveness of the integrated approach of machine learning (ML) techniques in predicting students’ academic progress. Predicting student accomplishment is crucial in matters of higher education, as well as machine learning, deep learning, and its connections to educational data. The proposed idea not only predicts student accomplishment but also makes it simpler for educators and administrators to monitor students so that they can provide assistance and incorporate the training for the best results. This study presents the view of students’ performance prediction models and explores several clustering and classification strategies that significantly enhance the accuracy of classification, particularly when a training dataset is accessible. Through the use of machine learning clustering and classifiers, such as Fuzzy C-Means, Stochastic Gradient Descent, Support Vector Machine, XGBoost, Gradient Boosting and K-Nearest Neighbors algorithms, we categorize instances as either indicative of a good or bad condition. As a result, our classification models demonstrate high accuracy in predicting students’ performance disorder outcomes.