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Classrooms play a pivotal role in students’ learning, and maintaining optimal indoor air quality is crucial for their well-being and academic performance. Elevated CO₂ levels can impair cognitive abilities, underscoring the importance of accurate predictions of CO₂ concentrations. To address the issue of inadequate analysis of factors affecting classroom CO₂ levels in existing models, leading to suboptimal feature selection and limited prediction accuracy, we introduce the RF-TPE-LSTM model in this study. Our model integrates factors that affect classroom CO₂ levels to enhance predictions, including occupancy, temperature, humidity, and other relevant factors. It combines three key components: random forest (RF), tree-structured Parzen estimator (TPE), and long short-term memory (LSTM). By leveraging these techniques, our model enhances the predictive capabilities and refines itself through Bayesian optimization using TPE. Experiments conducted on a self-collected dataset of classroom CO₂ concentrations and influencing factors demonstrated significant improvements in the <i>MAE</i>, <i>RMSE</i>, <i>MAPE</i>, and <i>R</i>². Specifically, the <i>MAE</i>, <i>RMSE</i>, and <i>MAPE</i> were reduced to 2.96, 5.54, and 0.60%, respectively, with the <i>R</i>² exceeding 98%, highlighting the model’s effectiveness in assessing indoor air quality.