The aim of this paper is to provide the survey of various methods implemented for the student performance prediction. This survey provides massive analysis of 25 research articles concentrated on student performance prediction techniques and the analysis is provided based on various scenarios and key points. Accordingly, the categorization of the techniques for student performance prediction techniques is made based on several approaches, such as machine learning-based techniques, deep learning-based techniques, clustering-based method, and fuzzy-based methods. Moreover, the analysis is provided based on the classification techniques, applied dataset, employed software tools, published year, and performance measures. At the end, the challenges of prevailing methods are defined to obtain improved contribution in developing novel performance prediction methods.