The capacity to predict and assess the academic performance of students has significantly enhanced educational results and aided educators in their profession. Using machine learning (ML) approaches, this research ascertains the important components influencing the achievement of students. With the use of classifiers such as K-Nearest Neighbors (KNN), Gradient Boosting, Naive Bayes, Decision Tree, Random Forest, Support Vector Machine (SVM), and Logistic Regression, the model provides significant prediction abilities. Methods of data preparation like scaling and the Synthetic Minority Over-sampling Technique (SMOTE) are applied to reduce class imbalances and enhance model performance. Gradient Boosting Classifier obtained the highest accuracy of 0.84 percent followed by Random Forest with an accuracy of 0.80 percent. The main factors determining academic achievement are identified by feature significance analysis. Our approach places a strong emphasis on using student performance data analysis for strategic decision-making. By gleaning crucial information from complex and unstructured data, we can support educators in proactive intervention and efficient resource allocation. The feature analysis provides clear insights into how these important elements affect students’ overall performance by highlighting how variables like Average Attendance and Average Study Time per Day can have a major impact on academic results. This accounts for Explainable AI (XAI) and this integration can help with the early identification of students who are at-risk and the application of targeted support techniques to improve their academic performance.