This study explores the use of Google Earth Engine (GEE) for efficient and robust classification of Earth’s surface. GEE’s cloud-based platform offers extensive satellite imagery and computational capabilities, making it ideal for large-scale geospatial analysis. The research covers preprocessing steps, feature extraction methods, and the implementation and optimization of classification algorithms within GEE, including Support Vector Machine (SVM) and Naive Bayes. Techniques for evaluating classification accuracy, addressing class imbalances, and optimizing model parameters are discussed. Real-world case studies demonstrate the versatility and effectiveness of GEE for Earth surface classification across diverse geographical contexts. This paper serves as a comprehensive technical guide for practitioners and researchers, providing insights and methodologies to facilitate accurate and scalable analysis of the Earth’s surface and its dynamic features, ultimately contributing to advancements in remote sensing and geospatial analysis.