With a growing emphasis on indoor air quality (IAQ) in educational environments, CO2 monitoring in classrooms has become commonplace. CO2 data can be used to estimate outdoor air change rate (ACH) based on the mass balance principle, which can be further linked to human health, performance, and building energy consumption. This study used a novel machine learning method to automatically segment CO2 concentration time series data into build-up, equilibrium, and decay periods, and then estimated classroom ACH using the corresponding CO2 mass balance equations. This method, applied to 40 classrooms in two mechanically ventilated K-6 schools, generated up to ten ACH estimates per day per classroom. A comparison with ACH calculated using the mechanical ventilation rates with 100% outdoor air reported by the building automation system during the study period reveals a slight underestimation by the decay and build-up methods, while the equilibrium method produced closer estimates. These differences may be attributed to uncertainties in occupancy, activity, CO2 emission rates, and air mixing. This research underscores the potential of leveraging CO2 data for more comprehensive IAQ assessments and highlights the challenges associated with accurately estimating ACH in real-world settings.