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Background: The United States is required to control the epidemic of HIV by rapidly detecting and responding to clusters of its transmission. Starting to be used recently, the development of molecular surveillance, digital contact tracing, and data integration has changed the definition of outbreak intelligence, allowing rapid transmission networks to be identified nearly in real time. Objective: This narrative review sums up the evidence published in the past two years on the shift in U.S. cluster detection and response models, which focus on technological, operational, and ethical aspects of the matter that affect preparedness to outbreaks and precision prevention. Approach: The critical analysis of peer-reviewed articles and population health assessments (2020-2025) was conducted to reveal the emergent trends, gaps in knowledge, and issues in implementation. Instead of systematically listing cases, the review focuses on interpretive synthesis of molecular epidemiology, network-based contact tracing, and community engagement frameworks. Findings: Recent national implementation of the use of molecular cluster identification by the CDC and local jurisdictions has reduced response times and improved cross-sector coordination. Nonetheless, the differences in the capacity of the workforce, interoperability of data, and ethical governance are still significant obstacles. Some of these innovations, like AI-assisted outbreak forecasting and community-engaged surveillance models, have potential and need to be empirically validated and deployed equally. Conclusion: To improve the response to HIV outbreaks in a short time frame, the coordination of their data, sound ethical control, and permanent cooperation with the community are required. The experience of the U.S. molecular and contact-tracing models suggests that technological accuracy and social trust have to be balanced to provide sustained control of the epidemic.