K Lakshmaiah , V Umera Sulthana , N. Sujitha , S Ramya Sree , K Sravani 1 2 (April - June) Satellite imagery plays a vital role in various fields including agriculture urban planning disaster management and environmental monitoring Efficient and accurate classification of satellite images is essential for extracting valuable information and making informed decisions In this study we propose the use of artificial intelligence techniques for satellite image classification A comprehensive dataset of labelled satellite images is collected representing different land cover types or objects of interest The dataset is preprocessed to enhance the image quality remove noise and normalize the data Data augmentation techniques such as rotation scaling and flipping are applied to increase the dataset size and improve the models generalization ability Future research directions may include exploring advanced deep learning architectures such as attention mechanisms or graph neural networks to further improve the classification performance Additionally the integration of multisensor satellite data and temporal analysis can enhance the capabilities of the classification models for dynamic monitoring and change detection applications Copyright (c) World Journal of Pharmaceutical Seiences. All rights reserved 2026