Globally, governments and the higher education industry are concerned about students’ employability. These educational institutions throughout the world are finding it more difficult to place students in relevant courses. The existing educational system finds it challenging to identify the features that have the greatest influence on students’ employability. Students must be equipped with employment at the end of the course in order to maximize their employability potential. To reduce the skills gap between students and the requirements of a competitive global economy, numerous educational institutions, academics, and the government are developing strategies. To streamline the development of these strategies, large-scale student data analysis is facilitated with machine learning techniques. In this context, ensemble models, which combine several algorithms to increase forecast accuracy, provide a robust framework for assessing these complex datasets. This study analyzes and projects the employability of the students based on research papers that were published between the years of 2020 and 2023. The transition from education to employment is a critical phase for students, and understanding the features influencing their employability is predominant. The objective is to identify key features influencing the employability of undergraduate engineering students in computer science.