["Transactions in GIS, Volume 30, Issue 5, August 2026. ", "\nABSTRACT\nNoise in urban areas poses a significant threat to urban dwellers. Accurate noise modeling is crucial for urban planning, environmental governance, and resident well‐being. Given the diversity of noise sources and the complex variations in both spatial and temporal dimensions, selecting appropriate noise modeling methods is crucial. In this paper, we aim to comprehensively analyze urban noise modeling methods and provide recommendations for method selection in different scenarios. We review over 100 studies and categorize urban noise modeling methods into three categories: theory‐driven methods, spatial interpolation methods, and machine learning methods. Theory‐driven methods based on sound source generation and propagation principles are primarily applied to traffic noise assessment in real‐world scenarios. Spatial interpolation methods, through mobile monitoring and crowd‐sourcing data collection techniques, fully utilize the spatial correlation of noise data, which has significant advantages in constructing dynamic noise maps. Machine learning methods can integrate multiple data sources, including remote sensing imagery and street view images, which are widely applied in urban noise modeling currently. We trace the evolution of noise modeling methods and evaluate their limitations. A practical recommendation is proposed for researchers, and future research will focus on data fusion and AI‐based real‐time monitoring systems.\n"]