AI logistics systems reduce delivery costs 15-25% and improve on-time delivery rates significantly. Route optimization: Vehicle Routing Problem (VRP) solved with Google OR-Tools using constraint programming - handles time windows, vehicle capacities, driver hours regulations. For real-time with dynamic orders: use heuristics (nearest neighbor + 2-opt improvement) for speed, exact algorithms for overnight batch planning. ETA prediction: gradient boosting models using GPS trajectory data, traffic patterns, weather, and historical delivery data - achieve 85%+ accuracy within 15-minute windows. Driver behavior: telematics data + ML detecting harsh braking, rapid acceleration, speeding - reduces fuel consumption 10-15% and accident rates. Predictive maintenance: IoT sensors (engine temperature, vibration, brake wear) + XGBoost predicting component failure probabilities, enabling proactive maintenance scheduling. Last-mile delivery: ML predicting successful delivery probability by address, time slot, and delivery history to optimize attempts. Dynamic capacity planning: demand forecasting + optimization for vehicle and driver scheduling.