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Abstract Background In the Republic of Ireland, the herd-level incidence of bovine tuberculosis (bTB), caused by Mycobacterium bovis, reached 6.40% by June 29th 2025, highlighting bTB’s risk to animal health, biosecurity and the economy. The complexity of bTB epidemiology, driven by multiple host species, undetected transmission and incomplete diagnostic sensitivity, makes surveillance and control challenging. Pathogen whole-genome sequencing (WGS) can clarify transmission dynamics but is constrained by the slow, variable mutation rate of M. bovis. This pilot case study integrates WGS with epidemiological data to elucidate transmission event pathways and could be a starting point for future automation. A decision-tree framework was developed to classify likely transmission event pathways by integrating M. bovis WGS sourced from the BTBGenie research project and epidemiological data. As proof-of-concept, one farm with multiple isolates was randomly selected from a national WGS database. Twenty-eight near-identical isolates (pairwise ≤ 3 SNP divergence), from other herds, were identified across databases of the Republic of Ireland and Northern Ireland, with full metadata was available for 16 isolates. These were analysed using TracebTB, a research tool linking national animal health, movement, wildlife and land management databases. Results Transmission event pathways for this case herd were classified as “local area transmission” (43.75%), “within-herd transmission” (12.5%), and “cattle movement-associated transmission” (also 43.75%), divided into between-herd (25%) and within-herd (18.8%) transmission. No evidence of residual within-herd transmission was found in this herd. Homebred animals served as spatial anchors, delineating the cluster’s ‘home range’ or kernel. A spatially distant homebred case, alongside the overall home range size, suggested an undetected movement-associated transmission event, likely via undetected carriers. Conclusions Integrating WGS with detailed epidemiology enables identification of probable bTB transmission event pathways, revealing undetected infections and highlighting biosecurity concerns associated with undetected carriers. The decision-tree framework provides a scalable approach for retrospective outbreak investigation, targeted surveillance, and efficient resource allocation, particularly in high-risk systems such as Controlled Finishing Units. These findings highlight the importance of transboundary collaboration in persistent bTB hotspots. Automating this approach could support validation of disease epidemiological models, guide targeted interventions, and optimising resource allocation, supporting Ireland’s goal of bTB eradication.