Water quality in river systems is strongly influenced by land use changes; however, spatial patterns of pollution in rapidly developing watersheds remain insufficiently understood. This study investigates the relationship between land use change and river water quality in the Jeneberang Watershed, Indonesia, through an integrated approach combining field-based monitoring and GIS-based spatial analysis. A total of seventeen monitoring stations were established along a 29.51 km stretch of the river, representing upstream, midstream, and downstream sections, with observations conducted from November 2024 to May 2025. Key parameters analyzed included temperature, dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), total nitrogen (TN), and total phosphorus (TP). Water quality was evaluated using national standards and the Pollution Index (PI) method, while spatial distribution patterns were analyzed using the Inverse Distance Weighting (IDW) interpolation technique in ArcGIS. The results reveal pronounced seasonal variability in water quality. During the dry season, pollution levels remained low, with PI values ranging from 0.35 to 0.41, indicating good water quality conditions. In contrast, the rainy season showed a significant deterioration, with PI values increasing from 1.70 to 8.50, corresponding to lightly to moderately polluted conditions. The highest pollutant concentrations were observed in downstream areas, particularly at Station 16, where TSS reached 948 mg/L, BOD 43 mg/L, and COD 290 mg/L. Spatial analysis demonstrated a clear correlation between pollutant distribution and land use patterns, highlighting the impact of agricultural activities and urban expansion on water quality degradation. Although this study is limited by its short-term observation period and does not capture long-term climatic or hydrological variability, it provides a comprehensive framework for linking land use dynamics with river water quality. By integrating field measurements with geospatial modeling, this study offers a novel contribution to understanding spatial pollution dynamics in tropical watersheds and provides valuable insights for watershed management, including erosion control, pollution mitigation, and sustainable land use planning.