In the pursuit of enhancing student success, Higher Education Institutions (HEIs) are increasingly focused on identifying and supporting at-risk students. This paper details the development of a comprehensive Early Warning System (EWS) designed for an engineering faculty to proactively identify and address at-risk students and subjects. The study employed a three-phase multi-method approach. Phase One involved a review of university policies, faculty rules, and previous practices to establish the foundation for the EWS. Phase Two utilized data from a Microsoft Excel-based tool, which gathered inputs across eight engineering departments concerning mark uploads, Learner Management System (LMS) utilization, identification of at-risk students, and planned interventions and Phase Three consisted of collecting qualitative data through departmental mid-subject review meetings, documenting feedback and observations that inform EWS enhancements. The research framework was informed by the sequential process model proposed by (Saad, M., Mazen, S., Ezzat, E., & Zaher, H. (2013). Towards a conceptual framework for early warning information systems (EWIS) for crisis preparedness. In Á. Rocha, A. Correia, T. Wilson, K. Stroetmann (Eds.), Advances in information systems and technologies. Advances in intelligent systems and computing (vol. 206). Springer. https://doi.org/10.1007/978-3-642-36981-0_48 ), which includes four procedural steps. These are ‘Collecting Information’, ‘Identification of Indicators’, ‘Forecasting’, and ‘Issuing Warnings’. Key findings highlight the current practices, pinpoint challenges, and identify opportunities to refine subject review processes. The study emphasizes the role of data-driven decision-making and collaborative efforts in addressing issues related to underperformance. Ultimately, the proposed EWS framework aims to improve student success rates and academic outcomes within HEIs, contributing to ongoing discussions on effective strategies to enhance student achievement.