Soil nitrous oxide (N₂O) fluxes are commonly measured with nonsteady state chambers using slope values derived from linear or quadratic regression, fitted to the change in N₂O concentration over time (dC/dt); however, these methods frequently underestimate N₂O flux values. Here, we propose a decision tree-based model (DTBM) to better match curve shape with linear and nonlinear models to estimate dC/dt. The DTBM was compared with linear, quadratic regression, and the Hutchinson–Mosier (H–M) equation. The objectives were to (i) evaluate curve shape classification; (ii) evaluate dC/dt response to uncertainty, and (iii) determine method effect on cumulative N₂O emissions and emission factor. Curve shapes with increasing N₂O concentration over time had the highest proportion of data (52%–55%). Mean N₂O flux calculated with DTBM showed to be less responsive to data variability, and therefore, more stable than the other methods. Data classification included in DTBM offered an improved method for calculating cumulative N₂O emissions in low-flux situations, whereas under a high-flux situation, all methods tested were acceptable to calculate N₂O emissions. The DTBM proved to be a robust method of matching each data type with the best model for calculating an individual flux and to accurately calculate cumulative N₂O emissions.