Objectives: This study aimed to develop and evaluate a deep neural network (DNN)-based predictive model for periodontal disease expenditures using community-level health environmental factors derived from public data. Methods: A total of 1,020 monthly records from 17 regions between January 2020 and December 2024 were analyzed. Independent variables included health behaviors, demographic characteristics, socioeconomic factors, and healthcare resource accessibility. A DNN model was constructed and evaluated using the mean absolute percentage error (MAPE), while permutation feature importance (PFI) was applied to quantify the relative contribution of each variable. Results: The DNN model achieved a mean MAPE of 11.01% (range: 9.35–12.51; SD: 0.83) across 10 repeated trials, indicating good predictive performance according to the Lewis (1982) criteria. PFI analysis identified total population, proportion of single-person households, and gender ratio as the most influential predictors of periodontal disease expenditures. Conclusions: These findings suggest that periodontal disease expenditures are shaped by complex interactions among demographic, socioeconomic, and behavioral factors, which can be effectively captured by AI-based predictive models. This study provides preliminary evidence that healthcare resource allocation and oral health policy development can benefit from AI-based approaches utilizing publicly available data.