This thesis presents the modeling and a reference architecture of a digital twin for immunization services in primary healthcare centers. The proposed system is grounded in concepts and technologies associated with Industry 4.0, including the Internet of Things (IoT), machine learning, and cloud computing. The modeling, developed using the Unified Modeling Language (UML), aims to establish a reference framework for the workflows and processes involved in immunization services, such as temperature monitoring and vaccination coverage tracking. The proposed architecture, aligned with the ISO 23247 standard, is structured into four domains: Observable Manufacturing, Device Communication, Digital Twin Platform, and User Domain. The system enables the storage, monitoring, and visualization of data generated within immunization rooms, particularly those related to temperature control in vaccine storage equipment, such as refrigerators and thermal boxes. In addition, the architecture supports the analysis of patients’ vaccination status in relation to the official immunization schedule, the identification of coverage gaps, and the provision of information to support strategic decision-making. The potential benefits of the proposed digital twin include the optimization of vaccine storage conditions, continuous monitoring of population vaccination status, reduction of dose wastage, and improved planning of immunization actions. Finally, this work discusses the feasibility, advantages, and potential future impacts of adopting digital twin technology in immunization services, as well as its applicability to other processes within Primary Healthcare.