While infrastructural development is still growing tremendously, immediate concern is on a responsive road network prediction framework for quickevolving environmental and social demands. In the conventional road planning methodology, the heavy reliance on time consuming and laborintensive manual surveys and data analysis in urbanization processes that are fastgrowing has made it increasingly inefficient. After extensive exploration, this paper presents a very innovative application based on the architecture of UNet, which utilizes geospatial data to predict road networks that could actually exist. In training the model, river networks, residential area data, and elevation data were used. Using Focal Loss with a pos_weight tensor, the model will care more about smaller roads and remote residential areas than before; these may be underrepresented when considering complex urban and rural landscapes due to class imbalance. Results have shown that while the model is limited with the data sources and computational power of the day and hence predicts large roads relatively well, it still demonstrates the full potential an AI model has in road network development and expansion. It improves regional connectivity that can help boost economic growth as well as contribute toward strategic smart city planning. This study represents significant development in road network prediction technology and especially points out its importance in improving the economic and social infrastructure by optimized road network construction.