Tropical lakes such as Lake Sentarum in Kalimantan, Indonesia, represent ecologically rich ecosystems with high biodiversity and constitute the largest lake on the island of Kalimantan. This lake serves as a sensitive indicator of climate change; however, its monitoring is often hindered by persistent cloud cover. This study evaluates the effectiveness of a Gradient Tree Boosting machine learning model integrated with multisource satellite data, including optical imagery, Sentinel-1 SAR, Sentinel-2, and high resolution NICFI data, in accurately mapping surface water dynamics. The Gradient Tree Boosting model was trained and validated using water and non water samples collected from annual imagery spanning 2019 to 2024, achieving validation accuracies ranging from 80 percent to 97 percent. Results demonstrate that Gradient Tree Boosting successfully integrates the strengths of each sensor, producing consistent annual water maps despite extreme hydrological fluctuations caused by El Niño and La Niña events. These findings highlight the model's potential application in water resource management, particularly in providing accurate baseline data to support adaptation planning for droughts and floods in climate vulnerable regions. • Utilized optical and radar remote sensing data for accurate spatiotemporal monitoring of seasonal and interannual changes in water extent. • Gradient Tree Boosting model to classify water and non-water areas with high accuracy. • Integrated Google Earth Engine for efficient cloud-based processing and time-series analysis of large geospatial datasets. • Provided insights into climate-driven hydrological patterns to support sustainable water resource management in tropical lake.