The rapid expansion of satellite constellations, aerial sensing platforms, Internet of Things (IoT) devices, and environmental monitoring networks has led to an unprecedented growth in Earth Observation data, commonly referred to as Big Earth Data. This transformation is reshaping how governments, researchers, and industry monitor the planet, enabling continuous observation of environmental changes at regional and global scales. Artificial Intelligence (AI), particularly machine learning and deep learning algorithms, has become an essential tool for extracting meaningful information from these vast datasets. AI-driven approaches are increasingly used to automate land cover classification, detect environmental changes, monitor natural disasters, forecast weather and climate patterns, support precision agriculture, assess water resources, and improve urban planning. These technologies significantly reduce processing time while increasing the accuracy and scalability of geospatial analyses. The integration of AI with cloud computing, high-performance computing infrastructures, and open-access satellite missions such as Copernicus is creating new opportunities for near real-time environmental intelligence. At the same time, important challenges remain, including the need for standardized datasets, improved interoperability between platforms, explainable AI models, data quality assurance, cybersecurity, and ethical governance of automated decision-making systems. As Earth Observation continues to evolve, AI is expected to become a cornerstone technology supporting climate resilience, disaster risk reduction, sustainable development, and evidence-based policymaking.