The Internet of Things (IoT) has become popular across various applications. Still, it can also contribute to air pollution through increased energy consumption, electronic waste, and emissions from manufacturing and data centers. Enhanced logistics and traffic management can lead to more vehicle use, worsening urban air quality. This paper introduces a novel approach for predicting air quality in smart cities using the Fed-BiLSTM model, which stands for Federated Bidirectional Long Short-Term Memory. As urban areas adopt IoT technologies, accurate air quality monitoring and forecasting are essential for addressing environmental challenges. The proposed Fed-BiLSTM model leverages federated learning for secure, decentralized training across diverse data sources, ensuring privacy while enhancing prediction accuracy. Experimental results indicate a notable enhancement in the accuracy of AQI predictions—approximately 35%, 7%, and 10% better than traditional SVR, RFERF (ML models), and RNN (DL model) approaches, respectively. This work supports efforts to create sustainable urban environments by enabling informed decision-making through reliable air quality forecasts.