For more than 50 years, marine and remote sensing researchers have investigated the methods of bathymetry extraction by means of active (altimetry) and passive (optics) satellite sensors. These methods, in general, are referred to as satellite-derived bathymetry (SDB). With the advances in sensor capabilities and computational power and recognition by the International Hydrographic Organization (IHO), SDB has been more popular than ever in the last 10 years. Despite a significant increase in the number of studies on the topic, the performance of the method is still variable, mainly due to environmental factors, the quality of the deliverables by sensors, the use of different algorithms, and the changeability in parameterization. In this study, we investigated the capability of Gokturk-1 satellite in SDB for the very first time at Horseshoe Island, Antarctica, using the random forest- and extreme gradient boosting machine learning-based regressors. All the images are atmospherically corrected by ATCOR, and only the top-performing algorithms are utilized. The bathymetry predictions made by employing Gokturk-1 imagery showed admissible results in accordance with the IHO standards. Furthermore, pixel brightness values calculated from Sentinel-2 MSI and tasseled cap transformation are introduced to the algorithms while being applied to Sentinel-2, Landsat-8, and Gokturk-1 multispectral images at the second stage. The results indicated that the bathymetric inversion performance of the Gokturk-1 satellite is in line with the Landsat-8 and Sentienl-2 satellites with a better spatial resolution. More importantly, the addition of a brightness value parameter significantly improves root mean square error, mean average error, coefficient of determination metrics, and, consequently, the performance of the bathymetry extraction.