This article presents a comprehensive approach to air quality monitoring by employing a deep learning-based framework that integrates both spatial and temporal data. The escalating issues of air pollution due to urban expansion and industrial activities necessitate advanced monitoring systems capable of providing detailed and dynamic insights into air quality. Our model leverages the capabilities of deep learning to assimilate and analyze data from various sources including satellite imagery, ground-based sensors, and meteorological stations. The framework processes spatial data to discern geographic pollution patterns and temporal data to track pollution trends over time. We detail the development and validation of this model, which employs layers designed to capture and interpret complex data relationships effectively. The results demonstrate the model's ability to provide accurate, real-time assessments of air quality. This capability is crucial for enabling timely decision-making and effective policy formulation for environmental health and safety. The study underscores the potential of deep learning technologies to transform air quality monitoring and offers insights into their practical implementation and future advancements in environmental science.