This study investigates land use/land cover (LULC) dynamics in New Damietta City, Egypt, over the period 2018–2025 using Sentinel-2 MSI Level-2A imagery at 10 m spatial resolution and a GeoAI workflow implemented in ArcGIS Pro. Three classes were mapped, namely Built-up Area, Vegetation, and Bare Land. The methodology combined supervised classification, independent accuracy assessment, and post-classification change detection within a unified city-scale framework covering 4,395 ha. A comparative evaluation of candidate classifiers showed that Random Forest outperformed the tested alternatives, achieving an overall accuracy of 85.0% and a Kappa coefficient of 0.77, compared with 76.2% and 0.65 for SVM and 72.5% and 0.59 for KNN. Accordingly, Random Forest was retained as the final classifier for generating the 2018 and 2025 LULC maps. The results indicate that built-up area increased from 1,614 ha (36.7%) in 2018 to 1,698 ha (38.6%) in 2025, corresponding to a net gain of 84 ha, while bare land decreased by 111 ha and vegetation showed a net increase of 27 ha. Cross-tabulation further revealed that Bare Land to Urban conversion was the dominant transition pathway, accounting for 558 ha. These findings demonstrate that GeoAI-based classification in ArcGIS Pro provides a reliable and operationally practical framework for monitoring urban expansion and green-space dynamics in planned coastal cities.