Satellite anomalies are strongly influenced by extreme space weather events, including geomagnetic storms, solar flares, and bursts of charged particles, which can severely disrupt satellite functionality. However, current satellite monitoring systems often lack integration between key space weather indicators, including the Kp index, Dst index, F10.7 solar flux, particle flux measurements, and satellite anomaly records, resulting in delayed responses and limited predictive capability. This study revitalizes the Satellite Anomaly Information System (SIAS) into a web-based predictive platform and has been used to diagnose proton and electron enhancements during severe geomagnetic storms in July 2000 and October 2003. The enhanced SIAS adopts a modular Laravel–Flask architecture, automates the parsing of over 7 GB of yearly satellite and space weather datasets from authoritative sources such as NOAA and OmniWeb, and delivers real-time anomaly monitoring, CRUD operations, and predictive services through REST APIs. Load testing with 100 concurrent users demonstrated system stability with response times under one minute. The system’s predictive model enables faster identification of vulnerable satellites, improving response times and anomaly management. Key contributions of this research are: (1) integration of real-time space weather data and satellite anomaly records, (2) development of a scalable Laravel–Flask backend architecture, and (3) empirical validation of system performance through extensive load testing. Limitations, including frontend latency during large-scale queries, have been identified for future optimization. Overall, SIAS lays a strong foundation for advancing predictive satellite anomaly monitoring systems or early warning systems in the face of increasing space weather challenges. It can help satellite operators and scientists take preventive measures to reduce future operational satellite failures.