<p>Demand-responsive transit (DRT) has emerged as a flexible mobility solution for addressing service blind spots in areas underserved by conventional public transportation. However, empirical research on DRT demand estimation remains limited, especially in the context of regional heterogeneity and zero-inflated demand. This study proposes a data-driven DRT demand estimation framework using operational records from two contrasting regions in Incheon, South Korea. Yeongjongdo is a tourism- and airport-oriented area with a high floating population and strong temporal variability, while Geomdan New Town is a residential district with relatively stable travel patterns. A grid-based origin–destination (O–D) modeling structure was employed, incorporating spatial variables such as land use, population dynamics, facility distribution, and public transport accessibility. Multiple region-specific machine learning models were developed and evaluated to estimate planning-oriented mean daily demand for each O–D-hour. A comparative analysis of alternative model configurations showed that the appropriate model structure varied according to regional demand conditions. In Yeongjongdo, the proposed two-stage model, which combines demand-occurrence classification and conditional regression, achieved the best overall performance, with a test MAE of 0.0022 and RMSE of 0.0105. These values represented reductions of 37.1% and 42.0%, respectively, relative to the strongest direct regression benchmarks. In contrast, direct regression was more effective in Geomdan New Town, achieving a test MAE of 0.0071 and RMSE of 0.0190. These results indicate that the relative performance of direct regression and two-stage prediction may vary across regional demand contexts.</p>