The development of Digital Twin technology has become a crucial advancement in smart city modeling, enabling precise urban planning, infrastructure monitoring, and autonomous vehicle navigation. This research presents a comprehensive workflow for creating a high-fidelity Digital Twin of road infrastructure by integrating Mobile Mapping System (MMS) data, including 3D point clouds and panoramic images. A total of 1,434 km of diverse road networks were surveyed using high-precision LiDAR and GPS-IMU sensors, with Ground Control Points (GCPs) established to validate positioning accuracy. The accuracy assessment demonstrated an average horizontal error of 0.06 m and a vertical error of 0.09 m, confirming the reliability of the collected data. To enhance visualization and usability, an RGB-colored point cloud was generated by aligning panoramic images with LiDAR data, followed by the transformation into a 3D mesh model. A Web-Based Visualization platform was developed using open-source tools such as Potree and Cesium, allowing seamless interaction with spatial data for real-time analysis. The integration of Artificial Intelligence (AI)-based segmentation techniques was explored to improve feature extraction and automation in Digital Twin creation. The findings of this research highlight the effectiveness of MMS-derived data in producing highly accurate Digital Twins for smart city applications.