The rapid expansion of digital learning environments has necessitated the development of intelligent systems that enhance student support services in higher education. This study presents a Machine Learning-Based Student Complaint and Priority Determination Support System with Automatic Responder, designed to improve response efficiency, reduce delays, and enhance accessibility to academic and administrative information. The system leverages Knowledge Discovery in Databases (KDD), Data Mining (DM), and Artificial Intelligence (AI) to analyze student queries, classify complaints, and provide automated responses based on a structured knowledge base. To ensure a structured and efficient development process, the system was designed using the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology, which provides a systematic approach to data understanding, preparation, modeling, evaluation, and deployment. Additionally, Object-Oriented Programming (OOP) principles were employed to ensure modularity, scalability, and maintainability of the system components. Traditional student support mechanisms in Nigerian universities, particularly in distance learning, are often semi-automated, relying on email-based communication and manual responses, which lead to inefficiencies, delays, and a lack of structured prioritization. To address these limitations, this research designs a smart ticketing system integrated with a machine learning classifier that categorizes and prioritizes student inquiries based on urgency and relevance. The system features automated response and tracking, and self-service functionalities that enable students to receive immediate resolutions to common queries. A Decision Tree Classification Model was implemented to analyze historical complaint data, establish relationships between student inquiries, and improve automated response accuracy. The model dynamically learns from past interactions, allowing the system to continuously enhance its predictive capabilities. By implementing graph-based relationships among tickets, the system identifies recurring queries and delivers intelligent responses without human intervention. The new framework significantly reduces response time, improves the quality of student support, and enhances the student-university interaction experience. This research contributes to the advancement of Educational Data Mining (EDM) by demonstrating how AI-driven student support systems can optimize information retrieval and streamline administrative processes