This paper presents the development and transformative impact of an innovative unified predictive monitoring framework designed for a large-scale retail fulfillment system. By seamlessly integrating cutting-edge industry-standard tools such as Grafana, Splunk, Kibana, and Elasticsearch, the framework provides unprecedented real-time visibility into fulfillment operations. The system leverages advanced machine learning algorithms, including time series forecasting, anomaly detection, and classification models, to proactively identify and resolve potential issues, particularly during high-demand periods. This data-driven approach has dramatically improved system stability, reducing service interruptions by 30% and enhancing customer satisfaction scores by 15%.