Maritime transport is a globally interconnected mode of logistics that enables large-scale cargo movement across nations. Unlike land or air transport, ships operate in an open and borderless domain, which necessitates the adoption of standardized operational frameworks across countries. However, this global standardization also constrains the rapid integration of emerging digital technologies into maritime operations. Recently, international organizations, such as the International Maritime Organization (IMO) and the International Hydrographic Organization (IHO), have been actively promoting next-generation maritime navigation systems and the implementation of unified data standards, such as the S-100 framework. These efforts align with global environmental goals, including decarbonization and sustainable shipping. In this study, we propose an integrated framework for estimating ship fuel consumption by utilizing detailed oceanographic information predicted through the Coastal Korea Operational Oceanographic System. The predicted fuel usage is intended to be delivered to ship operators in real time to assist in navigation decisions. Furthermore, referring to environmental forecasts, the system is designed to provide optimized navigation guidelines, with the ultimate aim of reducing fuel usage and improving overall operational efficiency. The present work is focused on establishing the data acquisition infrastructure and preliminary integration of real ship sensor data with high-resolution oceanographic forecasts, laying the foundation for future AI-based optimization models.