This paper explores the use of machine learning in optimizing campus recruitment (CR) strategies. It focuses on the impact of academic performance and work experience. The exploration of the research is based on prediction models created by machine learning. The data analytics approach involved a three-step data exploration, testing, and interpretation process. In the data exploration phase, patterns, relationships, and outliers were identified by analyzing the relevant data. Next, the data was subjected to rigorous testing to validate the hypotheses and determine the statistical significance of the findings. The most accurate prediction for the Machine Learning model is 88%. In addition to providing valuable insight into CR optimization strategies, this paper suggests several areas in which further research could be conducted. To improve the accuracy of CR prediction models and reduce the risk of selecting unfit candidates, Python models were trained to predict whether a student would be placed. This paper uses secondary data from a business school’s placement season in an Asian business school. This research provides a deeper understanding of the CR process and valuable insights for academics and business professionals. Furthermore, colleges and universities can use this research to develop strategies that improve student engagement and ensure successful hiring.