The article addresses cross-platform integration of predictive analytics with fulfillment and logistics in volatile, social-commerce–driven supply chains. Relevance stems from the need to shift from reactive fixes to anticipatory coordination across OMS, WMS, and TMS. Novelty lies in a unified architecture that binds heterogeneous data feeds to shared forecasts and prescriptive triggers across operational nodes. The study describes the integration blueprint and quantifies gains in forecast accuracy, inventory efficiency, and on-time delivery. The analysis examines e-commerce demand signals, IoT telemetry, weather/traffic feeds, and enterprise records; particular attention is paid to early-spike detection and predictive rerouting. The objective is to formalize a deployable framework and evidence its performance. Methods include comparative synthesis, conceptual modeling, and case-based benchmarking against industry baselines. Sources encompass peer-reviewed work on AI/ensembles and supply-chain optimization, industry reports, and practitioner guides. The conclusion details improvements and implementation guidelines. The article benefits researchers and operations leaders designing predictive fulfillment.