This study focuses on assessing pollutants concentrations in the Mediterranean Sea using satellite imagery and products. Traditional methods for measuring pollutants are labor-intensive, expensive, and have limited spatial and temporal coverage. As a result, satellite observations have become an effective solution for monitoring seawater across expansive areas. While low spatial resolution satellite datasets enable surface-level measurements, their restricted accuracy and low-resolution present challenges in detecting localized changes in coastal zones. To enhance the spatial resolution and accuracy of sea pollutants assessment, we have proposed an information technology based on machine and transfer learning techniques, which applied to pilot areas of the HORIZON Europe IMERMAID project in the Mediterranean Sea. Our approach integrates low-resolution satellite data with ground-based in-situ data and Sentinel-2 satellite imagery. Within the study we investigated key challenges in processing both satellite and ground data, conducting a comparative analysis among various satellite datasets (Sentinel-1, Sentinel-2, Sentinel-3, GCOM-C/SGLI) and ground datasets to identify the most informative indicators influencing sea pollutants’ levels. We used machine learning algorithms, particularly Random Forest and Multi-Layer Perceptron to develop an information technology that improves the spatial resolution of sea pollutant concentration maps by leveraging Sentinel-2 satellite optical data. This technology allows to generate maps of sea pollutants, such as chlorophyll-a, with a spatial resolution of 10 m for the pilot areas of the iMERMAID Horizon Europe project in the Mediterranean Sea. Due to the lack of data for the pilot regions the common approach is based on a transfer learning technique.