Stray dog populations present significant challenges to public health, urban safety, and animal welfare, particularly in regions experiencing rapid urbanization without sufficient veterinary infrastructure. Accurate estimation and monitoring of these populations are essential for developing targeted management strategies, including vaccination, sterilization, and intervention prioritization. Effective control requires not only robust data but also the implementation of evidence-based protocols and sustained follow-up to mitigate risks such as rabies transmission and dog bites. This paper offers a comprehensive bibliometric review of the academic literature on stray dog control, with a primary focus on the application of artificial intelligence (AI) for surveillance, identification, and decision support. The review identifies research trends, advancements, and existing gaps, and it highlights regional disparities in knowledge production relative to disease burden. AI-driven applications, including computer vision for breed and animal identification, spatial risk modeling for disease surveillance, and predictive analytics for population dynamics, enhance the capabilities of management programs. These tools improve topological speed, accuracy, and efficiency in hotspot detection, intervention prioritization, and outcome assessment, positioning AI as an integral component of data-driven program outcome assessment and public health strategies for stray dog management.