<p>The review presents 50 empirical studies to determine the effectiveness of predictive analytics in blended learning situations. State of the art machine learning models, including the neural networks with temporal graphs and transformer based models have a high predictive accuracy (82–91) when multimodal educational data is present. Three key success factors that should be listed are (1) the integration of multiple types of data, such as behavioral records, academic performance, and social learning indicators; (2) development of models in collaboration with instructors; and (3) reasonable ethics that ensure a high level of privacy and the restriction of the level of algorithmic bias. There are still a few operational issues such as the model performance decreases by the week and is very low as compared to non-traditional students. Such constraints can be overcome by promising new technological possibilities like federated learning, and real-time model calibration. On the findings synthesized, we would provide a three-componential implementation plan (1) adoption of interpretable AI systems in the entire institution; (2) continuous development of the teaching staff; and (3) flexible policy frameworks to make the algorithms equitable. The results show the fundamental element of human centred design in predictive analytics, and maintaining equity in learning outcome in the technological integration process.</p>