Air population is the primary concern in most urban areas because of its notable impact on the economy and health across the universe. The emergence of industry and automobiles made air pollution worldwide, which causes a highly critical issue and a more significant impact on humans' health than the contaminants. It causes health-related problems like lung-related diseases, namely respiratory problems and cardiovascular disease, and increases cancer. Accurate monitoring of air quality is of great importance to daily human life. The consuming time delay long-term delay is essential for long-term predictions. In this article, proposed Enriched Spatial-Temporal Sequence (EISAE-DL) improves the prediction accuracy by considering the long time delay based on locations and compared for experimental algorithm Improved Sparse Auto Encoder with Deep Learning (ISAE-DL) The experimental results show the effectiveness of the proposed EISAE-DL in terms of accuracy, precision, sensitivity, specificity, Area Under Curve (AUC), and Matthew's Correlation Coefficient (MCC).