PM2.5 pollution is a major health concern, especially in areas lacking robust real-time monitoring and predictive capabilities. This study presents a comparative analysis of three forecasting models—Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Temporal Convolutional Networks (TCN)—to predict PM2.5 concentrations in four regions of Kigali, Rwanda. Utilizing a dataset spanning from late 2020 to 2024, these models were trained on historical air quality data obtained from sensors. Our findings reveal that the LSTM model consistently outperforms both TCN and ARIMA models, delivering lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) in predicting PM2.5 levels. These results underscore the effectiveness of LSTM models in providing more accurate air quality forecasts in complex temporal environments. This research lays the groundwork for enhancing air quality monitoring and public health strategies in Rwanda.