High-quality animal health surveillance data are essential for timely disease detection, effective control strategies, and evidence-based decision-making. This study assessed the quality and functionality of animal health surveillance data in Dilla Zuria District, Southern Ethiopia, using document review of Disease Outbreak and Vaccination Activity Reports (DOVARs) from 2019 -2023 and key informant interviews with animal health workers. Data flow, completeness, accuracy, utilization, and laboratory integration were evaluated against established surveillance quality indicators. Of the 60 expected monthly DOVAR reports, 53 (88.33%) were available, indicating relatively high reporting completeness; however, 96.2% of reports were zero reports, suggesting substantial under-reporting of disease events. Only two outbreaks (Lumpy Skin Disease and Blackleg) were documented, both with critical data quality gaps, including missing outbreak identifiers, population at risk, and case counts. No routine data analysis by animal, place, and time was conducted at the district level, and none of the animal health workers had received recent surveillance training. Although laboratory support existed, sample submission was infrequent and diagnostic feedback was delayed. Overall, the surveillance system was operational but limited in effectiveness due to weak data utilization, poor outbreak documentation, and inadequate capacity building. Strengthening analytical practices, training, laboratory integration, and data quality monitoring is essential to improve surveillance performance and animal health outcomes in the district.