The global logistics and transportation industry is in the midst of a digital transformation, driven by the rapid rise of e-commerce, globalization, and increasing demand for more dynamic supply-chain systems. Modern-day conventional logistics still faces many challenges: inefficient load utilization, cargo spoilage due to poor monitoring, frequent empty return trips, lack of real-time tracking, and fragmented technological implementation. Traditional logistics solutions rely on separate systems: GPS tracking for location, basic cold-chain tools for cargo freshness, and independent booking platforms for scheduling, all operating in isolation with limited coordination.

The project's aim is to address these problems using a Smart IoT-Based Logistics Management System that unifies Artificial Intelligence and Internet of Things technologies into one intelligent

ecosystem. IoT sensors, such as GPS, temperature, vibration, and cameras, are deployed inside transport vehicles in order to monitor the cargo status on a continuous basis. These sensors transmit real-time data to the cloud across secure protocols, thus allowing end-to-end visibility. AI algorithms will analyze the streaming data to optimize truck load space, ensure safety of cargo, and predict potential backhaul opportunities to minimize empty returns. The digital twin interface takes it further by providing an interactive virtual representation of the truck and cargo, showing environmental conditions, operational performance, and predictive insights.

The proposed system enhances operational efficiency through better load management, reduced risk of spoilage, higher fuel efficiency, and informed decision-making. It greatly enhances the transparency of fleets, reduces operational costs, and leads to environmental sustainability with reduced carbon emissions. The integration of AI + IoT follows a scalable approach that transforms traditional logistics into an intelligent, adaptive, and autonomous system