In today's world, technology is rapidly advancing. To stay up with this rapid technological advancement, graduates must have a solid skill set, including problem solving, effective communication, and the ability to quickly adapt to new technology, among other things. These talents will be impossible to teach using traditional classroom teaching methods. As a result, a successful education system must be built to suit these needs."Outcome-Based Education" is one such educational approach that has received a lot of attention from professors and scholars all around the world in recent years [1]. Students' self-learning takes precedence in the Outcome Based Education System above learning from what teachers have considered in the classroom. In outcome-based education, students are given goals to achieve by the end of each course, as well as goals to achieve by the time they graduate. In outcome-based education, lecturers serve as knowledge facilitators for students. In this paper, random forest based ranking algorithm is used to design the framework for classification of student performance based on OBE Framework. In outcome-based education, one of the most important inputs for determining the efficacy of a university's or college's teaching and learning activities is student feedback of direct and indirect method. The proposed system results are compared with traditional machine learning classification algorithms like LDA, K-nearest neighbor, CART, Naïve bayes and Support vector Machine. The proposed approach gets optimum accuracy compared with existing approach.