The rapid advancement of large language models (LLMs) offers new avenues for overcoming the limitations of current data-driven GIS analysis methods, giving rise to the emerging field of large language model-driven GIS analysis (LLM4GIS). This study presents a systematic review of 673 relevant research articles published between 2022 and July 2026. By tracing the five-stage evolutionary trajectory of GIS analysis methods and elaborating the framework for LLM4GIS, we conduct a quantitative bibliometric analysis that reveals the field’s rapid growth from sporadic exploration to explosive expansion, along with the shifting landscape of its dominant technical trajectories. Building on these analyses, we develop a taxonomy spanning four dimensions—technical approaches, datasets, applications, and evaluation—and systematically analyze the advances and evaluation methodologies across five core application areas: geospatial knowledge question answering, knowledge extraction, spatiotemporal reasoning, geospatial code generation, and analysis modeling. Key challenges and future research directions are further discussed along five dimensions: multimodal data integration, high-quality data development, domain-specific model development, integration of world models with real-world geospatial environments, and interpretability and trustworthiness. This review provides a systematic reference for advancing the bidirectional synergy between LLM4GIS and GIS4LLM and for fostering a new generation of spatiotemporal intelligence ecosystems.