Educational institutions are rapidly transitioning from traditional administrative workflows to data-driven, technology-enabled ecosystems. However, most institutional Enterprise Resource Planning (ERP) systems still operate reactively, offering limited predictive intelligence for academic performance, student retention, and resource optimization. This study proposes an integrated framework that combines Machine Learning (ML) models with institutional ERP architecture to create a predictive and adaptive education management system. The framework leverages multi-modal ERP data attendance logs, assessment records, LMS interactions, financial transactions, and administrative workflows to generate early-warning indicators, performance forecasts, and automated decision support. A hybrid ML pipeline using Random Forest, Gradient Boosting, and LSTM networks is designed to capture both static attributes and temporal learning behaviors. The system additionally incorporates adaptive feedback loops that adjust academic interventions based on real-time analytics, allowing faculty and administrators to visualize risks, optimize schedules, and personalize student support. Experimental validation demonstrates that the integrated ML-ERP model achieves high accuracy in predicting dropout risks, performance deviations, and workload bottlenecks across three institutional datasets. The results underscore the transformative potential of embedding ML within ERP platforms to shift education management from reactive reporting to proactive, intelligent planning. This study establishes a scalable blueprint for next-generation educational ERP systems