Abstract <p>Cities aiming for Industry 5.0 must contend with a persistent bottleneck: urban mobility. We propose a data-driven traffic management approach that couples the Internet of Things (IoT) with large-scale analytics to improve sustainability, operational efficiency, and environmental outcomes. Our framework instruments are critical assets, signalized intersections, sensing and surveillance networks, and public transport nodes, with IoT devices that stream continuous vehicle and pedestrian activity. The resulting multimodal signals are standardized and modeled with predictive algorithms that flag emerging congestion, adapt signal timing, and support dynamic rerouting to reduce delay. Using heterogeneous, real-world datasets, we observe consistent gains in mobility performance and measurable reductions in emissions, while also enabling evidence-based planning for infrastructure investments. Beyond these technical results, the work advances a shift toward mobility systems that are adaptive, human-centric, and ecologically responsible. In sum, the study shows how IoT-enabled data pipelines and predictive models can operationalize the goals of Industry 5.0 by delivering safer, faster, and more sustainable urban travel.</p>