Abstract: The AI-based Logistics Management System is a pioneering solution that leverages the force of artificial intelligence and machine learning to transform logistics. It is a comprehensive system integrating various modules driven by artificial intelligence to optimize logistics processes, including demand forecasting, route optimization, real-time tracking, and supply chain visibility. The system allows logistics stakeholders to make data-driven decisions based on predictive analytics and machine learning algorithms, enabling them to predict possible disruptions and thus mitigate risks proactively. The advanced route optimization module by the system uses AIdriven insights to derive the most efficient routes, thereby minimizing fuel consumption, lowering emissions, and reducing delivery times. Realtime tracking provides end-to-end visibility for the logistics manager, thus tracking shipments and inventory, while he is always ready to respond to exceptions and disruptions. Additionally, the demand forecasting module of the system utilizes machine learning algorithms for the analysis of historical data, seasonal trends, and external factors to predict demand, and logistics stakeholders can optimize their levels of inventory, reduce the possibility of stockouts, and minimize waste. The AI-based Logistics Management System also has a supply chain visibility module that offers real-time insights into inventory levels, shipment status, and performance in the supply chain. This allows logistics stakeholders to identify bottlenecks, optimize inventory allocation, and improve overall supply chain efficiency. Moreover, the advanced analytics module of the system provides actionable insights, allowing logistics stakeholders to measure key performance indicators, identify areas for improvement, and optimize logistics operations to meet evolving business needs. Market Basket Analysis, Hybrid Algorithm, FP-Growth, ECLAT, Neural.

Keywords: AI Logistics, Logistics Management System, Supply Chain Optimization, Route Optimization, Real-time Tracking, Demand Forecasting, Predictive Analytics.

| DOI: 10.17148/IARJSET.2025.12105