Abstract
The integration of Artificial Intelligence (AI) with Geographic Information Systems (GIS) marks a pivotal advancement in spatial data processing, offering enhanced predictive capabilities and real-time analytical precision. This study provides an in-depth review of the evolution of AI applications in GIS, examining current methodologies and future prospects. Employing a qualitative research approach that encompasses a systematic literature review, expert interviews, and analysis of policy documents, the study identifies critical trends, challenges, and opportunities in AI-GIS integration. The findings reveal that the application of deep learning techniques particularly Convolutional Neural Networks (CNNs) has significantly improved spatial analysis accuracy, enabling efficient risk mapping and disaster mitigation. Additionally, AI-driven automation in data collection and processing has streamlined GIS operations, although it introduces challenges related to algorithmic bias and data privacy. The development of advanced geospatial models, including interactive 3D visualizations, further supports comprehensive urban planning and resource management. Nonetheless, the need for standardized data formats and robust computational infrastructure remains imperative. This paper concludes by offering strategic recommendations for future research, emphasizing multidisciplinary collaboration, the establishment of ethical frameworks, and the enhancement of data integration protocols. The insights presented aim to inform both academic inquiry and practical implementations, ensuring the responsible evolution of AI-driven GIS to address complex spatial challenges