Educational administration in large and resource-constrained school networks is frequently constrained by fragmented records, delayed reporting, data inconsistencies, and limited analytical capacity for evidence-based decision-making. This study presents the development and evaluation of a data mining-enabled Schools Monitoring System (SMS) designed for the Bauchi State Science and Technical Education Board (BSSTEB), Nigeria, which supervises 521 secondary and technical schools. The study adopted a design science research approach, complemented by agile development principles and the CRISP-DM framework for data mining. System requirements were informed by stakeholder interviews and observation of existing administrative workflows. The resulting centralized web-based platform integrated school, teacher, student, reporting, monitoring, and analytical functions. Educational data were prepared through data cleaning, k-nearest-neighbor imputation, outlier treatment, normalization, and feature engineering. The complete educational dataset comprised 21,400 records, of which 16,000 were used for the student-performance classification analysis. The decision-tree classifier achieved 86% overall accuracy, with a weighted F1 score of 0.845 and 85.70% accuracy under 10-fold cross-validation. K-means clustering identified four school-performance groups, with a silhouette score of 0.65 and a Davies–Bouldin index of 0.72. Multiple linear regression achieved an R² of 0.78 and an RMSE of 8.45 points for examination-score prediction. Functional testing produced a 97.2% pass rate, while user acceptance reached 92%, and System Usability Scale (SUS) performance was 82.5/100. Operational evaluation indicated substantial improvements in report generation, data retrieval, admission processing, and record accuracy compared with the previous manual workflow. The findings demonstrate that integrating interpretable data mining into a centralized school-monitoring platform can strengthen educational administration, improve operational efficiency, and support proactive decision-making in resource-constrained environments.