Analyzing land use and land cover (LULC) change is critical for understanding environmental impacts and informing urban planning. This study investigates LULC changes in Mandalay Township over the years 2014 to 2023, employing deep learning with a CNN model and a Support Vector Machine (SVM) model on geospatial data. Satellite imagery and ancillary data from these years were used to classify six classes: built-up areas, bare land, water, wetland, plantation forest, and herbaceous. The output results included two classification maps and an accuracy assessment. In this classification, the SVM model classifier showed the best accuracy assessment, so this classification was used to identify change area, change type map, and change detection map. The main aim of this paper is to monitor and detect land use and land cover changes in Mandalay Township based on deep learning with a CNN model and an SVM model classifier over 10 years.