Impact of Farm Management Practices on Salmonella Occurrence at the Farm Level—A Blend of Traditional Methods and Artificial Intelligence

<b>Background</b>: <i>Salmonella enterica</i> remains a leading cause of foodborne illness worldwide despite decades of advances in surveillance and control. Traditional interventions have targeted specific points in the food chain, yet recurrent outbreaks show that <i>Salmonella</i> exploits system-wide gaps and inconsistencies. <b>Methods</b>: This review synthesises recent evidence from epidemiology, experimental microbiology, and regulatory practice to evaluate how management decisions, from farm through processing, influence <i>Salmonella</i> risk in livestock-derived foods. <b>Results</b>: Poultry, pig, and cattle farms employ targeted measures, including rodent control, litter management, batch rearing, and secure feed storage, to reduce contamination. The greatest reductions in <i>Salmonella</i> prevalence occur when these measures are embedded in coherent farm-to-fork programmes. Future gains are likely to come less from novel interventions and more from rigorous implementation, integration, and the validation of existing tools, supported by high-resolution surveillance (including whole-genome sequencing) and prevention-focused management systems. Artificial intelligence can enhance control through real-time surveillance, predictive risk modelling, and targeted interventions informed by diverse farm data. <b>Conclusions</b>: Sustained progress in <i>Salmonella</i> control will depend on rigorously applying existing interventions, supported by high-resolution surveillance and prevention-focused management. Carefully governed AI can enhance real-time monitoring and risk prediction, but its value hinges on addressing data, cost, and regulatory challenges.