This chapter introduces a digital twin system designed to address the complex transportation challenges faced by Bangkok. As the capital of Thailand, Bangkok struggles with chronic traffic congestion, air pollution, and inefficiencies in its public transport infrastructure. The proposed system integrates real-time data collection, advanced simulations, and AI-powered analytics to create a dynamic virtual replica of the city's transport network. Key components include a data acquisition layer, a data integration platform, a simulation engine, an analytics module, and a visualisation interface. The framework supports applications such as traffic flow optimisation, public transport planning, predictive maintenance, and air quality monitoring. Lessons drawn from case studies in Singapore, Helsinki, and Amsterdam provide strategic insights for implementation. The chapter also considers future directions, including the integration with broader smart city initiatives and ethical considerations in data governance. While focused on Bangkok, this digital twin framework offers a scalable model for other Asian cities facing similar urban mobility challenges. It signifies a step towards a more connected, efficient, and sustainable urban future in Asia, positioning the region as a leader in shaping global smart city innovation.