Land cover change reflects regional development dynamics driven by urbanization and infrastructure expansion and may lead to inconsistencies with the Regional Spatial Plan (RTRW). The objective of this study is to identify spatiotemporal patterns of land cover change and urban growth in North Gorontalo Regency using Sentinel-2 imagery and vegetation indices (NDVI and NDLI), and to evaluate their consistency with the Regional Spatial Plan (RTRW). The analysis employed Sentinel-2A imagery from 2016 and 2025 via a spatial–temporal approach. The land cover classification accuracy was assessed via a confusion matrix, whereas the urban growth direction was analyzed via the standard deviational ellipse (SDE). Land cover change modeling was conducted via the CLUE-S model with a weight-of-evidence (WoE) approach, and the results were validated via receiver operating characteristic (ROC) curves. The results indicate that built-up areas increased by approximately 1,470 ha (82.12%) between 2016 and 2025, mainly through the conversion of agricultural land and fishponds. Urban expansion exhibits a dominant east–southeast orientation, forming a linear pattern along major transportation corridors. The integrated use of NDVI and NDLI identifies vegetation–nonvegetation transition zones, capturing early-stage urban encroachment that is not detected using NDVI alone. Modeling results show that NDVI and distance to roads are the primary drivers of land cover change, while elevation and distance to government centers are secondary. The CLUE-S model performs well, with an overall accuracy of 98.79%, a kappa coefficient of 0.78, and an ROC value of 0.71. This study is limited by the use of two temporal observations and the exclusion of dynamic socioeconomic variables. Scientifically, the findings demonstrate that integrating NDVI–NDLI analysis with CLUE-S–WoE modeling reveals spatiotemporal urban growth patterns and driving mechanisms beyond conventional single-index approaches, while providing a quantitative basis for improving RTRW evaluation.