Attrition or better known as student dismissal or drop out from completing courses in higher learning institutions is prevalent in higher learning institutions in Malaysia and abroad.There are several reasons attributed to the attrition in the context of student in higher learning institutions. The degree of attrition varies from one institution to another and it is cause for concern as there will be a lot of wastage of resources of academic and administrative besides the adverse effect on the social aspect. In view of this, minimizing the attrition rate is of paramount importance in institutions.There have been numerous non technical approaches to address the issue, but they have not been effective to predict at early stage the likelihood of students dropping out from higher learning institutions. Technical approach such as data mining has been used in predicting student attritionby some researchers in their past research work.However, not all prediction data mining techniques and other relevant and significant factors attributed to student attrition have been fully explored to address the issue. As of result this, this paper will focus on using support vector machine model to predict student attrition. Itwillalso examinerelevant and other factors that contribute to the attrition among students in Malaysia. With all these in place, a model with high accuracy in predicting student attrition is expected to achieve.