The growing threat of cyberattacks on Internet of Things (IoT) networks has led to the exploration of advanced technologies to improve security. Therefore, this systematic review evaluates the efficacy of honeypots in IoT networks for detecting and preventing intrusions and identifying attack patterns to strengthen network security against cyberthreats. Based on the established inclusion and exclusion criteria, 16 articles were selected from the Scopus database. The results demonstrated improvements in terms of performance and accuracy, reaching values above 80%, reduction of false positives and false negatives reaching values of 5%, and better identification of different types of attacks and machine learning techniques to dynamically adjust their configuration for real-time threat detection. In conclusion, honeypots are promising tools for intrusion detection in IoT networks. Successful implementations can identify specific vulnerabilities and attacks, thus strengthening the security of such networks. In future research, it will be essential to focus on the scalability and adaptation of these technologies to address the increasing complexity of threats in the IoT environment.