Motorcycle-enabled crimes, such as theft and assaults, challenge urban safety, with 241,697 thefts in Mexico (20152022) and 1,537 homicides in Guatemala since 2012. This study proposes a layered IoT and RFID framework to deter such crimes in smart cities, integrating RFID-tagged vehicles, helmet sensors, geo-fencing, and AI-enhanced cameras. Grounded in Routine Activity Theory (guardianship) and Diffusion of Innovations Theory (adoption), the framework comprises sensing, edge processing, integration, and governance layers. Routine Activity Theory (guardianship) and Diffusion of Innovations Theory (adoption), the framework comprises sensing, edge processing, integration, and governance layers. A systematic review and digital twin simulations in Mexico City demonstrate a 25% interdiction accuracy gain. RFID achieves 94.7% detection rates, while edge analytics minimize latency (0.8s/alert). Though costs and cybersecurity risks exist, the design remains modular and scalable. “Static zones,” referring to areas with fixed surveillance and limited mobile adaptability, often fail to deter fast-moving crimes. In contrast, digital twins—virtual urban replicas—enhance predictive planning by simulating offender trajectories, sensor placement, and optimal patrol response, increasing intervention accuracy. The framework also advances social legitimacy, defined here as public acceptance rooted in legal safeguards, transparency, and participatory design—key factors for technology adoption in low-trust areas. Keywords: IoT, RFID, smart cities, motorcycle crime, public safety, digital twin.