Air pollution remains a critical environmental and public health challenge in urban areas, which requires accurate and efficient predictive models to mitigate its impact. This study introduces a novel spatiotemporal model, Graph Temporal LSTM (GT-LSTM), which integrates Machine Learning (ML) techniques to forecast air pollution levels, with a focus on Particulate Matter (PM) \(_{2.5}\) concentrations. By combining Graph Convolutional Network (GCN) to capture spatial dependencies and Long Short-Term Memory (LSTM) to model temporal patterns, the proposed framework provides precise and localized predictions in urban and suburban regions. Our analysis demonstrates the competitive predictive capabilities of the model, achieving high Coefficient of determination ( \(R^2\) ) and low error values, highlighting its robustness in correlating predicted and observed pollutant levels. The GT-LSTM model effectively incorporates historical data, neighboring influences, and local pollution sources, allowing reliable short- and long-term forecasts, even in data-short environments. In addition to its predictive accuracy, the model prioritizes computational efficiency and scalability, using cost-effective sensor networks to expand coverage and reduce the dependence on traditional data sources. By offering fine-grained insights into air quality patterns, this approach supports real-time monitoring, long-term planning, and proactive decision-making, benefiting policymakers and urban residents alike. This study underscores the transformative potential of spatiotemporal modeling and ML techniques in enhancing air pollution monitoring systems, ultimately contributing to improved air quality management and public health outcomes.