The provision of essential resources and a fruitful existence depend on educational standards. Colleges and universities are integrating technology into their conventional teaching techniques in response to developing technologies such as artificial intelligence. Discovering hidden relationships in informative data and forecasting scholars’ academic achievement is now possible using Machine Learning (ML). The massive amount of student data is beyond the capacity of statistical methods and database management systems to process. Keeping this in mind, we examined the students’ dataset to see how different features affect student outcomes. The larger Kalboard 360 dataset includes academic and demographic features collected and used for the study. Data visualization analysis shows that students who belong to urban areas got high grades compared to rural areas and the female students’ performance is better than male students. In this study, we designed the models using Random Forest (RF), Support Vector Machines (SVM), Decision Trees (DT), K-Nearest Neighbor (KNN), and Logistic Regression (LR) to predict students’ academic achievement. For better performance, we applied a cross-validation technique to the ML models. The result indicates that the Decision tree-based model accuracy is 99% which is better than the other models. Finally, using the ML model in education could help spot learning gaps in students’ studies and identify underachievers early on. This will enable teachers to make well-informed decisions to improve students’ academic achievement.