Predicting the students grade which is emerged as a major area of investigation in education system due to the desire to identify the basal factors that impact academic capability .due to the success in predicting the grade point average (GPA), most of the prior research has focused on predicting grades in a unique set of classes based on students performances. The issues were associated with the data-driven models of GPA prediction are further louden by a small size and a relatively large number in dimension of observations in an experiment. we utilize the state-of-the-art machine and their learning techniques to construct and verify a predictive model of GPA based on a set of self-regulatory learning behaviors determined in a relatively small-sample experiment. The purpose of the artificial intelligence in the teaching and learning in higher education is to learn new technologies. It investigate the educational implications of the emerging technologies on the way of the students learning and institutions to teach and evolve those technologies. Recent technological advancements and the speed of adopting new technologies in higher education are explored in order to predict the future nature of higher educations in a world where the artificial intelligence, it is the part of the fabric of our university. They pinpoint are the some challenges for institutions of higher education and student learning of these technologies for teaching, learning, student support, and administration and explore further directions for research. Ultimately, the goal of the grade prediction in similar experiments is to make use of the already constructed models for the design for the intervention strategies aimed at helping the students at a risk of failure in academic. We lay the mathematical groundwork for defining and detecting probably helpful interventions using a probabilistic predictive model of the GPA. The purpose of self-regulatory behaviors is warranted, because the proposed interventions can be easily practiced by students.