Background: Dengue hemorrhagic fever (DHF) continues to pose a serious public health threat in Ho Chi Minh City, where recurrent outbreaks are sustained by complex socio-environmental and urban dynamics. Hotspot analysis of DHF is essential for guiding targeted interventions, as uniform citywide measures are often inefficient. This study applies spatial statistical techniques to detect hotspots of DHF during the 32nd epidemiological week of 2025. Methods: Confirmed DHF cases aggregated at the ward level were analyzed using descriptive mapping, boxplot visualization, and scatter plots to explore spatial heterogeneity and the relationship with population density. The Getis-Ord G_i^* statistic was employed to identify statistically significant hotspots (high-high clusters) and coldspots (low-low clusters). Significance testing at multiple confidence levels (95%, 99%, 99.9%) was used to validate cluster strength. Results: The descriptive analysis revealed a right-skewed distribution of DHF incidence, with most wards reporting low to moderate case numbers and a small subset exceeding 70 cases. The relationship between population density and case counts was weakly negative, indicating that density alone does not explain spatial variation. Hotspot analysis identified 20 wards as significant high-high clusters, concentrated in the central–northern urban core, and 13 wards as low–low coldspots in peripheral districts. Several wards showed highly significant clustering (p ≤ 0.01), highlighting critical foci that disproportionately contributed to the epidemic burden. Conclusions: Dengue hemorrhagic fever transmission in Ho Chi Minh City during Week 32, 2025, was characterized by localized hotspots embedded within a largely neutral background. These findings emphasize the utility of spatial statistics in detecting high-risk zones and provide actionable guidance for geographically targeted interventions. By prioritizing vector control, surveillance, and community engagement in hotspot wards, public health authorities can optimize resource allocation and enhance the effectiveness of dengue control programs.