Air pollution is a significant problem for people's health and the environment in rapidly growing cities, where many sources of pollution are constantly changing. Traditional monitoring systems struggle to spot emerging pollution trends in real time because they take too long to detect them, don't cover enough areas, and lack sufficient analytical tools. This study introduces EDP-PR, a comprehensive system for environmental data processing and pattern recognition, designed to address these limitations. EDP-PR will help identify air-quality problems more quickly and support efforts to lower risks before they occur. The Multi-Source Environmental Data Processing (EDP) is the first part of the EDP-PR architecture. It combines and processes data from sensor networks, weather variables, and emission indicators. The second part is the Pattern Recognition Engine (PRE), which uses supervised learning, clustering, and anomaly detection to locate pollutant footprints. The last section is the Rapid Risk Inference Module (RRIM), which uses how pollutants move through space and time to predict when risks may rise. Advanced machine learning algorithms were used to classify pollution events and identify events that precede surges in PM2.5, NO₂, and O₃. The models used were Random Forest, Gradient Boosting, and Hybrid LSTM-CNN. The proposed EDP-PR model surpassed conventional threshold-based systems by as much as 25 minutes in detection accuracy, with experimental results indicating a success rate. Pattern-recognition analysis enabled recommendations for targeted mitigation. The new method is around 18–25% more accurate than typical statistical models for finding pollution risks, according to experimental results. A decrease in reaction latency of over 20% and a decrease in false warning rates of roughly 15% make it possible to find pollution peaks earlier. These advances make it possible to use more effective and timely intervention tactics. In conclusion, the suggested approach improves the management of air pollution risks by giving quick, precise, and data-driven information. This leads to a nearly 22% overall improvement in risk reduction efficiency. This system helps preserve public health and the environment by controlling air quality in advance.