AI Enabled Student Dropout Prediction and Counseling Management System

Student dropout remains as a significant challenge for educational institutions, affecting institutional performance, financial stability, and student career outcomes. Traditional retention mechanisms are largely reactive and depend on manual monitoring resulting in delayed interventions. This paper proposes an AI enabled system for early student dropout prediction integrated with an intelligent counseling management framework. The proposed system leverages supervised machine learning algorithms to analyze academic, demographic, attendance, and behavioral data to identify at -risk students into risk levels and automatically recommends counseling interventions. Multiple classification models are evaluated using Accuracy, precision, Recall, F1-score, and ROC_AUC metrics experimental results demonstrate improved early- risk detection and optimized counseling workflow management. The system enhances proactive student retention strategies and supports data-driven institutional decision making. The result in higher retention rates happier students and a more efficient use of resources by adopting this approach institutions can shift from reactive to proactive support addressing issues before they escalate ensuring system is both effective and secure