Machine learning (ML) is a powerful artificial intelligence (AI) tool that has a potential to transform higher education by enabling accurate student success and retention predictions. Numerous studies presented several factors regarding the opportunities, challenges, and significance of ML. This paper identifies ML algorithms’ usage in diverse data sources, including academic records, engagement metrics, and sociodemographic characteristics, to forecast student outcomes. The study adopts a narrative review through randomly screened previous literature to address AI and ML over the last 10 years. The results show that predictive ML and AI models, such as Artificial Neural Network (ANN), Decision Trees, Hybrid Deep Neural Network (HDNN), and Clustering Algorithms, can be implemented in higher education institutions to identify at-risk students, allowing them timely interventions and personalized support to improve their academic success. This research study also addresses ethical considerations regarding data privacy and potential biases in ML models affecting specific student demographics. Findings revealed that AI-enhanced academic performance, continuous evaluation, and refinement are essential to mitigate student outcomes and ensure ethical data use in higher education. Educational stakeholders and researchers should implement effective policies to deal with the challenges of implementing AI-based and ML-based models.