As educational data continues to grow, the field of educational data mining has gained popularity. This field focuses on extracting hidden patterns from educational data, allowing for a better understanding of students, including their learning styles, and the ability to predict their academic performance. In order to forecast undergraduate students' academic success, this study proposes a model that incorporates data mining techniques. The researchers collected data through questionnaires that included information on demographics, prior GPA, and family history. The data was then analyzed using data mining models, like Decision Tree and Random Forest, and other methods in order to develop the most accurate prediction model. These findings highlight the significant factors that influence students' academic success.