Academic performance analysis plays a vital role in improving the quality of education by identifying students who may require additional academic support. With the rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML), educational institutions can analyze large volumes of student data to predict academic outcomes accurately. This review paper presents a comprehensive overview of AI-based techniques used for academic performance analysis, highlighting the role of machine learning algorithms in predicting student success and identifying at-risk learners.

The review examines widely used algorithms such as Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Logistic Regression, Artificial Neural Network (ANN), and XGBoost. These algorithms are compared based on their predictive accuracy, computational efficiency, interpretability, and suitability for educational datasets. The study also discusses commonly used AI tools and frameworks, including Python, Scikit-learn, TensorFlow, PyTorch, XGBoost, WEKA, and Jupyter Notebook, which support the development and evaluation of academic performance prediction models.

The findings indicate that ensemble and deep learning approaches, particularly XGBoost, Random Forest, and Artificial Neural Networks, generally achieve superior prediction performance compared to traditional machine learning methods. AI-driven academic performance analysis enables early identification of students facing academic challenges, supports personalized learning strategies, and assists educators in making data-driven decisions. Despite challenges such as data quality, privacy, and model interpretability, AI-based systems continue to transform educational analytics by enhancing prediction accuracy and improving student outcomes.

Keywords: Artificial Intelligence, Machine Learning, Academic Performance Analysis, Student Performance Prediction, Educational Data Mining, Random Forest, XGBoost, Artificial Neural Network, Predictive Analytics.