Indoor air quality (IAQ) is critical for health, comfort, and cognitive performance in classrooms. Yet, many classrooms in lower-middle-income countries (e.g., Bangladesh) rely on natural ventilation and lack any warning system for impending air-quality guideline breaches. This study is motivated by the need to maintain healthy learning environments in resource-constrained settings, where students and teachers are often exposed to elevated CO \(_{2}\) and particulate levels. To address this, we present the first comparative evaluation of three forecasting models: Prophet, Random Forest (RF), and Long Short-Term Memory (LSTM), on daily PM \(_{2.5}\) and CO \(_{2}\) time series covering six months of continuous operation in Dhaka, Bangladesh. Our goal is to anticipate IAQ deterioration to enable proactive ventilation or filtration interventions. The nonlinear machine-learning models substantially outperform the Prophet baseline. Random Forest performed best for PM. (RMSE 1.87 \(\upmu \textrm{g}/\textrm{m}^{3}\) , \(\textrm{R}^{2}\) 0.992), showing its ability to capture complex pollutant dynamics. LSTM excelled at forecasting peak CO \(_{2}\) (RMSE 159.7 ppm, R \(^2\) 0.693), which is critical for timely interventions. These findings demonstrate the feasibility of accurate, low-cost IAQ forecasting in resource-constrained classrooms and underscore the potential of data-driven forecasting to maintain healthier learning environments.