Air pollution, particularly fine dust, poses a significant threat to public health and necessitates accurate prediction models for effective mitigation strategies. In this paper, we propose an attention-based ConvLSTM-DNN networks for fine dust prediction. Our model integrates the feature extraction capabilities of a 2D Convolutional Neural Network (CNN) with the long-term memory retention of an LSTM, capturing spatial and temporal dependencies in the input data. We introduce an attention mechanism to enhance the model's predictive ability by assigning greater weight to influential regions or features. This mechanism improves overall accuracy. Additionally, we optimize pre-processing of time-series data to maximize performance. Using real-world weather and fine dust concentration data, we conduct experiments to evaluate our model. Comparative analyses with existing models demonstrate its superior prediction accuracy. Our study has implications for air quality management and public health. The proposed model offers accurate and reliable fine dust forecasts, aiding decision-making processes. By leveraging ConvLSTM, attention mechanisms, and optimized pre-processing, our model advances air pollution monitoring and control. In summary, our paper presents an attention-based ConvLSTM-DNN networks for fine dust prediction. Experimental results highlight its superiority and potential in improving air quality and safeguarding public health.