<![CDATA[Population density is a critical indicator in regional development planning because it is closely associated with public service distribution, transportation systems, healthcare provision, and environmental management. The rapid growth of digital technology has increased the volume and complexity of demographic data, requiring more effective analytical methods for population density analysis. This study aims to analyze the application of data mining in population density mapping based on studies published between 2021 and 2025. A systematic literature review approach was employed by examining 30 scientific articles obtained from Google Scholar, Semantic Scholar, and Crossref. The review process included article identification, literature screening, data collection, and analysis of findings based on the algorithms, research fields, data sources, and analytical methods used in the selected studies. The findings indicate that the most frequently applied algorithms were K-Means Clustering, DBSCAN, and Density Peaks Clustering. Data Mining and Machine Learning emerged as the dominant research field, representing 50% of the analyzed articles. The primary data sources used in the reviewed studies included public datasets, government data, and spatial imagery. The results also show that clustering was the most commonly applied analytical method in population density analysis. These findings demonstrate that clustering techniques are effective for supporting population density mapping and identifying spatial data distribution patterns relevant to regional decision-making. The study contributes to demographic and regional development research by synthesizing recent evidence on the role of data mining in population density mapping and providing a reference for future studies on spatial demographic analysis.]]>
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