Urbanization has significantly exacerbated traffic congestionTraffic congestion and air pollutionAir pollution, posing severe environmental and public health challenges. This study introduces an integrated framework that leverages machine learningMachine learning models, including Seasonal Autoregressive Integrated Moving Average (SARIMASARIMA), Support Vector MachinesSupport Vector Machines (SVM) (SVM), and Long Short-Term MemoryLong Short-Term Memory (LSTM) (LSTM), to predict pollutant levels and monitor traffic congestion. By employing real-time sensor data and predictive analytics, the system identifies critical congestion levels based on emissionsEmission of carbon monoxideCarbon monoxide (CO) (CO) and nitrogen oxides (NOx). The analysis demonstrates the superior performance of SARIMA in forecasting long- term trends, while LSTM effectively captures dynamic fluctuations. This interdisciplinary approach provides scalable solutions for urban pollution management, emphasizing the potential of artificial intelligence to enable sustainable smart city ecosystems.