Air quality significantly impacts public health, particularly in urban areas like Lucknow, where deteriorating air quality has been linked to severe health issues, especially in children and vulnerable groups. Accurate air quality prediction allows authorities to implement timely measures to shield these populations from harmful exposure. A lack of comprehensive data and robust algorithms has limited traditional forecasting methods. This study employs a Support Vector Regression (SVR) model to forecast pollutant levels and the Air Quality Index (AQI) in Lucknow using publicly available historical data from the Central Pollution Control Board (CPCB) and local monitoring stations. Among various configurations, the SVR model with a Radial Basis Function (RBF) kernel showed superior performance, achieving an accuracy of approximately 93.4%. Utilizing all available variables rather than relying on feature selection methods like Principal Component Analysis (PCA) improved prediction outcomes. The model effectively forecasts key pollutants, including sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), particulate matter (PM2.5 and PM10), and ground-level ozone (O3). This research demonstrates the potential of advanced machine learning techniques to address air quality challenges in Lucknow, offering valuable insights for policymaking and urban environmental management.