This research presents an integrated Warehouse Intelligence Framework that brings together four AI modules - dynamic route optimization with Traveling Salesperson and A* pathfinding algorithms; fire detection with YOLO, spread prediction with the project frame-danger data, and shelf proximity; predictive analytics with ARIMA/Prophet/LSTM and Gradient Boosting models for stock anomalies and worker performance classification; and Best-Fit bin-packing with 3D space visualization - on a unified MERN + Flask platform. The overall system demonstrated 25% less total picking distance; 91% mAP in fire detection; under 8% MAPE in stock forecasting; 92% accuracy in worker classification; and an 18% increase in cubic-meter utilization, all in real time (20+ FPS, sub-second rerouting, 99% uptime) using only existing CCTV infrastructure. This modular, cost-conscious approach which breaks down silos between efficiency, safety, prediction and space utilization allows warehouses to confidently enter the adaptive Industry 4.0 space without retrofitting or installing proprietary hardware.