By integrating IoT devices with outdoor air quality monitoring, smart cities can proactively respond to pollution hotspots, optimize resource allocation, and implement targeted interventions to mitigate the impact of poor air quality on citizens and the environment. In this context, deep learning can empower accurate outdoor air quality forecasting by analyzing complex data patterns, enabling proactive measures for pollution mitigation and citizen well-being in smart cities. To achieve the same, we present a unique air pollution forecasting model ProTSF, which is Bayesian optimization-based deep learning model Long Short-Term Memory (LSTM), to ensure high fidelity for any dataset in this article. The assessment findings show this model’s usefulness, which points to future uses in more smart city forecasting scenarios. We attained a remarkable average accuracy of 95.83 percent through daily forecasts, with an average Root Mean Squared Error (RMSE) value of 0.089 parts per million (ppm), which is the highest as compared to other well-established time series forecasting models (e.g., AR, MA, ARIMA, SARIMA) achieved till date.