Abstract The development of smart cities depends on intelligent systems that integrate data from diverse environments. In this work, we present ELiOT , an end‐to‐end LiDAR odometry framework with transformer architecture designed to utilize real‐world data, simulations, and digital twins. ELiOT leverages high‐fidelity simulators and digital twin environments to enable sim‐to‐real applications, training on the real‐world KITTI odometry dataset while benefiting from simulated data for improved generalization. Our self‐attention‐based flow embedding network eliminates the need for traditional 3D‐2D projections by implicitly modeling motion from sequential LiDAR scans. The framework incorporates a 3D transformer encoder‐decoder to extract rich geometric and semantic features. By integrating digital twin environments and simulated data into the training process, ELiOT bridges the gap between simulation and real‐world applications, offering robust and scalable solutions for urban navigation challenges. This work underscores the potential of combining real‐world and virtual data to advance LiDAR odometry and highlights its role for the future smart cities.