Urban forests are very important for the environment and for people, especially in semi-arid cities where there is not much greenery. This makes heat stress worse and makes the city less livable. This paper presents a comprehensive geospatial methodology for selecting afforestation sites in the expanding semi-arid urban area of & Scedil;anl & imath;urfa, Turkey, characterized by minimal forest cover, rapid urbanization, and extreme weather conditions. We identified nine ecological and infrastructure criteria using high-resolution Sentinel-2 images and features from the terrain. These criteria include slope, aspect, topography, land surface temperature (LST), solar radiation, flow accumulation, land cover, and proximity to roads and homes. After being normalized to make sure they were ecologically relevant and consistent, all of the datasets were put together into a GIS-based Multi-Criteria Decision Analysis (MCDA) tool. The Analytic Hierarchy Process (AHP) was then used to weight the criteria. A deep learning-based semantic segmentation model was used to create a thorough classification of land cover, primarily to exclude unsuitable areas such as dense urban fabric and water bodies. The final afforestation suitability map showed that 151.33 km2 was very suitable and 192.06 km2 was suitable, mostly in the northeastern and southeastern urban fringes. This was because the terrain and subclimatic conditions were good. The proposed methodology illustrates that urban green infrastructure planning can be effectively directed within climate adaptation frameworks through the integration of remote sensing and spatial decision-support tools, especially in ecologically sensitive and rapidly urbanizing areas.