Artificial Intelligence (AI) is rapidly transforming the landscape of geographical research by enhancing the efficiency, scale, and precision of spatial analysis. From land use classification and climate modeling to urban planning and disaster risk assessment, AI techniques such as machine learning (ML), deep learning (DL), computer vision, and natural language processing (NLP) are revolutionizing the way geographical data is processed and interpreted. The integration of AI with Geographic Information Systems (GIS), Remote Sensing (RS), and Big Data platforms has enabled researchers to extract meaningful patterns from vast, complex, and often heterogeneous datasets. This chapter provides a comprehensive overview of AI applications in geography, emphasizing both theoretical underpinnings and practical deployments. Key focus areas include land cover classification using convolutional neural networks (CNNs), spatial pattern detection via unsupervised learning, urban sprawl prediction using ensemble models, and integration of AI in climate change analysis and microclimate mapping. Furthermore, we discuss the challenges of interpretability, data bias, model generalization, and ethical considerations. Case studies from different regions illustrate the practical benefits and limitations of these applications. The chapter concludes by outlining future directions, advocating for hybrid models, real-time AI-GIS integration, and inclusive geospatial data governance to address emerging challenges. The findings aim to bridge the gap between technological advancement and spatial science, contributing significantly to the evolving paradigm of AI-powered geographic research.