Land-use and land-cover (LULC) classification is a crucial task in remote sensing, playing a vital role in environmental monitoring, urban planning, and resource management. Accurate classification of LULC helps in tracking changes in land usage, assessing environmental impact, and making informed decisions for sustainable development. With the growing availability of satellite imagery, machine learning has emerged as a powerful tool for automating this classification process. In this study, we explore the effectiveness of various ml techniques in predicting LULC using the EuroSAT dataset, which consists of 27,000 satellite images covering 10 different land-use categories. The dataset includes 13 spectral bands with a resolution of 64 × 64 pixels, making it a rich source of information for classification tasks. To build an effective classification model, we implement and evaluate six widely used ml algorithms: Random Forest, K-Nearest Neighbor, Support Vector Machine, Decision Tree, Gradient Booster, and an Ensemble Classifier. To assess model performance, we compare these classifiers based on key evaluation metrics, including accuracy, precision, recall, F1 score, and balanced accuracy. Among the individual models, the Ensemble Classifier demonstrates the highest accuracy, as it combines the strengths of multiple classifiers to enhance predictive performance. However, to further improve classification accuracy, we introduce a HM-Stacking Classifier, which integrates multiple models in a layered architecture. This approach allows the model to learn from the predictions of base classifiers and refine its final decision making process. This experimental results show that the hybrid Stacking Classifier significantly outperforms individual models, demonstrating its effectiveness in improving LULC classification accuracy. This study highlights the potential of hybrid machine learning techniques in satellite image analysis, offering a promising approach for automated LULC prediction in various real-world applications.