Addressing the United Nations Sustainable Development Goals, particularly sustainable cities and communities (SDG 11), and the protection of terrestrial ecosystems (SDG 15), is closely linked to understanding patterns of urbanization. Rapid urban growth significantly influences ecosystem functions, including transportation, housing, and economic development. Monitoring this growth and analyzing performance patterns are essential for supporting decision-making and guiding urban planning and management. This study presents an automatic approach for monitoring urban expansion by applying the Random Forest machine learning classifier from 2015 to 2025 using Google Earth Engine. The method exploits spectral indices not only for unsupervised classification but also for training the Random Forest classifier, thereby ensuring a fully automated workflow. The proposed approach is applied to Gharbia Governorate, a region which lacks surrounding desert margins and is instead entirely composed of fertile agricultural land, to monitor urban expansion in three-year intervals. The proposed study, which achieved a kappa coefficient exceeding 0.96 across all study periods, revealed a gradual decline in agricultural land from 75.5% in 2015 to 72.7% in 2025. These outcomes offer valuable insights to support evidence-based planning and promote sustainable land use management.