The integration of Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT) with geospatial technologies has revolutionized earth observation and resource management. This transformative synergy enables comprehensive, precise, and rapid data analysis, unlocking unprecedented capabilities across various domains, including climate monitoring, disaster management, precision agriculture, and natural resource assessment. Advanced tools like Convolutional Neural Networks (CNNs), automated feature extraction, classification algorithms, and change detection enhance the interpretation of complex datasets, offering multidimensional insights to address pressing global challenges. However, these advancements bring forth significant challenges categorized into social, ethical, and technical dimensions. Socially, the deployment of these technologies’ risks widening disparities due to unequal access and digital divides. Ethically, issues such as data privacy, algorithmic bias, and accountability in AI-driven decision-making necessitate transparent and explainable methodologies. On the technical front, the scalability of AI models, standardization of geospatial datasets, and the need for robust regulatory frameworks present considerable hurdles. This study critically examines these technical breakthroughs alongside their broader societal and ethical implications, emphasizing the importance of inclusive practices and responsible innovation. The research advocates for equitable access to geospatial tools, transparency in AI methodologies, and the establishment of stringent regulatory mechanisms to address these challenges effectively. By exploring the intersection of AI and geospatial sciences, this paper underscores the importance of fostering sustainability, accountability, and equity in harnessing the full potential of these technologies for transformative societal impact.