(untitled) (International Journal of Latest Technology in Engineering Management & Applied Science, 2026)
Container Freight Stations (CFS) in Chennai operate under increasing cargo volumes, fluctuating demand, and infrastructure constraints, often resulting in congestion and extended container dwell times. Artificial Intelligence (AI) provides data-driven solutions through predictive analytics, automation, and intelligent scheduling to improve operational efficiency. AI-based monitoring enables terminals to anticipate congestion before it occurs and dynamically allocate resources. In high-traffic ports like Chennai, real-time prediction of arrivals and yard occupancy prevents bottlenecks. Immediate decision support reduces delays, improves service reliability, and lowers operational costs. This study evaluates AI applications at All cargo Terminals, Chennai, using a mixed-method research design that integrates stakeholder surveys and statistical modelling. Findings demonstrate a strong relationship between yard utilization, staffing levels, and dwell time reduction. The results recommend AI-driven tools to sustain performance improvements and enhance terminal competitiveness. Identifying statistical relationships allows terminals to implement live dashboards that guide operational decisions instantly. Predictive tools support shift planning and yard allocation in real time. Continuous monitoring ensures consistent performance gains rather than temporary improvements.