Supply chain management has become one of the most important operational functions in modern businesses, especially functions in modern businesses, especially in global trade, e-commerce, and manufacturing industries. Efficient supply chain operations ensure that products are transported from suppliers to customers in the right quantity, at the right time, and at the lowest possible cost. However, many organizations continue to face challenges such as shipment delays, poor logistics coordination, inappropriate shipping mode selection, and regional delivery inefficiencies. These issues directly impact customer satisfaction, profitability, and brand reputation. A Random Forest Classifier was implemented to predict delayed deliveries based on shipment and transaction attributes. The model achieved an accuracy of 69.65%, with strong precision and acceptable recall performance. In addition to predictive modelling, an interactive Streamlit dashboard was developed to visualize logistics performance, monitor KPIs, and support business decision-making. The proposed system can help organizations proactively identify risky shipments, reduce delivery delays, improve operational planning, and enhance overall supply chain efficiency.