Summary The spread of highly pathogenic H5N1 avian influenza clade 2.3.4.4b among dairy cattle in the United States has raised concerns over the increased risk of major human outbreaks or even a pandemic. Thus, it is of critical importance to monitor the ongoing transmission and evolution of the virus within cattle, humans, and other infected animals residing in or near farms. Here, we propose to establish a real-time outbreak intelligence framework to facilitate genomic surveillance of the virus at the host- and population-level using data from individually infected hosts and aggregate samples from sources such as pooled milk. Our framework will consist of three core components with automated workflows to process raw data from public sources such as NCBI Sequence Read Archive, perform real-time phylogenetics, examine sequences for mutations of potential antigenic or functional significance, and develop an early warning system with a real-time analytics dashboard. We will also conduct real-time phylodynamic studies using Bayesian methods to understand the emergence and spread of the virus among dairy cattle in the United States. Further, we will ensure that any data newly collected through this work, and processed results from our framework are publicly available through Github and whenever applicable through repositories such as NCBI GenBank and discoverable through the NIAID Data Discovery Portal. Our proposed framework will provide critical insights into the ongoing evolution of the virus and serve as an early warning system for the emergence of any phenotypes with pandemic potential.