Predicting Graduate Employability Using Machine Learning: A Structured Research Assessment

Machine Learning (ML) has rapidly transitioned into a decisive computational tool for evaluating learner capabilities and predicting employability outcomes. In modern hiring ecosystems, graduate placement decisions increasingly depend on data-driven insights rather than intuition. This parallel research provides an in-depth analytical discussion on major predictive models, performance indicators, and institutional evaluation frameworks related to employability forecasting. Through synthesis of diverse research contributions, this paper highlights dominant predictors of career readiness such as academic standing, program relevance, internship exposure, and behavioural performance. Evidence indicates that ensemble-based algorithms, particularly Random Forest and XGBoost, consistently demonstrate high predictive precision compared to traditional linear approaches. Persistent challenges however—such as lack of unified datasets, feature imbalance, model interpretability, and ethical data concerns—continue to shape future research requirements.