The exponential growth of satellite imagery presents numerous potentials for Earth observation, yet traditional approaches have limitations in terms of automation, scalability, and precision. Deep learning-powered artificial intelligence (AI) -driven satellite image analytics revolutionizes remote sensing by making precise feature extraction, object detection, and semantic segmentation possible. To improve multi-spectral and hyper spectral image analysis for uses such as urban planning, disaster relief, and climate monitoring, this work investigates the integration of CNNs, ViTs, and GANs. We offer an AI-powered platform that conquers the problems caused by cloud computing, edge AI, and federated learning for effective evaluation of huge data to challenge the computational difficulties. The real-time environmental monitoring has been improved by integrating AI with platforms like the Google Earth Engine and the transfer and self-supervised learning, which boosts flexibility and versatility across the geospatial datasets. This study emphasizes the necessity of robust, open AI models to support sustainable decision-making, creating new avenues for geospatial analysis to facilitate global sustainability.