The safety of school networks has become increasingly critical in today’s digitally connected world due to the growing frequency and sophistication of cyber threats such as malware, viruses, and unauthorized intrusions. Educational institutions are particularly vulnerable because of their expanding reliance on digital platforms for administrative functions, online learning, and internal communications. In many universities, a significant challenge lies in the absence of efficient mechanisms to detect and respond to cyberattacks in their early stages often identifying breaches only after substantial damage has occurred. This research introduces a unified, tri-layered framework that combines real-time threat detection using the Random Forest algorithm, intelligent alerting for immediate incident response, and content filtering for policy enforcement specifically tailored to the operational and behavioral dynamics of educational networks. By utilizing real-time traffic data from the university’s own infrastructure, the system is trained to detect a wide range of threat patterns while reducing overfitting, a common challenge in cybersecurity applications. The framework represents a shift from traditional reactive models to a proactive, predictive security paradigm, enhancing the capability of Management Information Systems (MIS) teams to preemptively address cyber risks. This integration of technical robustness, contextual relevance, and administrative control renders the proposed system both practically effective and strategically significant. The outcomes of this study have important implications for the field of educational cybersecurity, offering a scalable and adaptable model that can serve as a foundation for future research and implementation across academic institutions.