<![CDATA[Abstract Inland container delivery constitutes a critical component of the global maritime logistics chain, acting as the final phase that connects international ports to inland destinations. Accurate prediction of inland container delivery times is crucial for enhancing operational efficiency, minimizing demurrage and detention costs, and improving customer satisfaction across global supply chains. Purpose –. This study leverages historical container movement data across key international ports to develop a robust machine learning model for predicting inland container delivery timelines. Methodology –. Using a Random Forest Regressor, the model was trained to forecast the total inland delivery time based on features such as container size, type, shipping line, dispatch weekday, and temporal patterns. Findings – The findings have practical implications for shipping lines, freight forwarders, port authorities, and inland terminal operators seeking to optimize logistics planning, reduce uncertainty, and improve supply chain. Evaluation of the model's performance yielded a Mean Absolute Error of 4.59 days, a Root Mean Squared Error of 10.55 days, and a coefficient of determination of 0.68, indicating moderate predictive accuracy. Supporting visualizations - including learning curves, gain curves, feature importance plots, residual distributions, and prediction bands - illustrate the model's strengths and areas for further refinement. Originality – The study contributes to the growing field of intelligent logistics and maritime informatics by providing a data-driven framework for improving inland delivery predictability]]>
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