Although the digitalization of internship management in higher education has advanced considerably, most existing systems remain reactive: they record what has already happened rather than anticipate where the process is likely to fail. This study reframes internship governance as a predictive task and proposes a four-layer early-warning model that converts routinely collected supervision data into forward-looking risk signals. Drawing on a conceptual-analytical methodology combined with expert appraisal, the model integrates a data layer, a feature layer, a machine-learning risk-scoring layer, and a tiered action layer that triggers graduated mentor responses. Comparative expert assessment indicates that a predictive configuration substantially outperforms the conventional reactive approach in the timeliness of risk detection, the prevention of internship dropout, and the objectivity of assessment. The findings suggest that shifting the locus of management from documentation toward anticipation can measurably improve both the continuity and the quality of professional practice. The proposed model offers a transferable framework for higher education institutions seeking to embed analytics-based decision-making into internship policy.