This study compares Support Vector Machine (SVM) and Random Forest (RF) machine learning methods for land use monitoring in the Patuha geothermal area using satellite imagery from Landsat 8 and Sentinel 2 between 2021 and 2023. The goal is to improve sustainable water management by accurately categorizing land cover types. The study evaluates the effectiveness of these techniques in maintaining water sustainability in geothermal regions. It emphasizes parameter refinement and model assessment to enhance land use classification accuracy. The research uses Kernlab and e1071 for algorithm comparison to produce a precise Land Use Model Map, highlighting the importance of advanced analytical techniques in environmental management. The study finds that RF outperforms SVM in terms of accuracy, stability, and precision, especially in complex urban settings, making it the preferred model for high-reliability tasks. The application of SVM and RF aligns with Sustainable Development Goals (SDGs) 6 and 15, promoting sustainable water management and ecosystem conservation. The study uses spectral indices like NDVI, NDBI, and NDWI to identify land cover types. It discusses parameter tuning, feature importance, and model evaluation metrics such as confusion matrix, Overall Accuracy (ACC), Kappa Coefficient (KC), Sensitivity, and Specificity. The results show that RF provides higher accuracy and reliability in land use classification, making it suitable for geothermal land use monitoring. The study concludes that integrating advanced machine learning techniques with remote sensing data is crucial for sustainable water and geothermal resource management. The findings support the need for integrated land and water management strategies to balance geothermal energy production with environmental conservation. The study highlights the importance of accurate land use mapping for maintaining hydrological balance and ensuring the sustainability of geothermal operations. The results demonstrate the effectiveness of RF in land use classification, contributing to the conservation of water resources and the achievement of SDGs 6 and 15.This study compares Support Vector Machine (SVM) and Random Forest (RF) machine learning methods for land use monitoring in the Patuha geothermal area using satellite imagery from Landsat 8 and Sentinel 2 between 2021 and 2023. The goal is to improve sustainable water management by accurately categorizing land cover types. The study evaluates the effectiveness of these techniques in maintaining water sustainability in geothermal regions. It emphasizes parameter refinement and model assessment to enhance land use classification accuracy. The research uses Kernlab and e1071 for algorithm comparison to produce a precise Land Use Model Map, highlighting the importance of advanced analytical techniques in environmental management. The study finds that RF outperforms SVM in terms of accuracy, stability, and precision, especially in complex urban settings, making it the preferred model for high-reliability tasks. The application of SVM and RF aligns with Sustainable Development Goals (SDGs) 6 and 15, promoting sustainable water management and ecosystem conservation. The study uses spectral indices like NDVI, NDBI, and NDWI to identify land cover types. It discusses parameter tuning, feature importance, and model evaluation metrics such as confusion matrix, Overall Accuracy (ACC), Kappa Coefficient (KC), Sensitivity, and Specificity. The results show that RF provides higher accuracy and reliability in land use classification, making it suitable for geothermal land use monitoring. The study concludes that integrating advanced machine learning techniques with remote sensing data is crucial for sustainable water and geothermal resource management. The findings support the need for integrated land and water management strategies to balance geothermal energy production with environmental conservation. The study highlights the importance of accurate land use mapping for maintaining hydrological balance and ensuring the sustainability of geothermal operations. The results demonstrate the effectiveness of RF in land use classification, contributing to the conservation of water resources and the achievement of SDGs 6 and 15.