<p>This study aims to develop a tool to help determine the cargo of a bulk carrier using data from the automatic identification system (AIS) from the moment the data is collected. Accurate cargo information is crucial for several operational and strategic decisions, such as setting freight rates, planning loading and unloading times at ports and assessing storage capacity at terminals. However, the availability of cargo data through AIS has been limited by privacy and regulatory issues. To achieve this goal, the methodology of this study involves collecting AIS data from a bulk carrier, including information on the ship's draft. Subsequently, an analysis of the data will be carried out and a model of a typical ship will be created to obtain the ship's hydrostatic curves. A multi-factor regression algorithm will then be developed to estimate the ship's load in real time based on the AIS data collected. This algorithm has been trained and validated to ensure its accuracy and applicability in different operational contexts. It is hoped that this study will contribute to the development of tools and methodologies to accurately estimate a ship's cargo using AIS data, helping companies, ports and regulatory authorities to make more informed and efficient operational and strategic decisions. The main goal is to develop a real-time tool for bulk carrier payloads based on AIS data.</p>