This article presents a comprehensive analytical framework designed to quantify and scale air pollution intensity using machine learning techniques. With the increasing impact of air pollution on public health and the environment, there is a pressing need for advanced methodologies that can provide accurate, scalable, and real-time assessments of air quality. Our framework addresses this need by integrating diverse data sources, including air quality measurements, meteorological data, emission inventories, and geospatial information, to construct a detailed representation of pollution patterns. The core of our framework involves a series of methodologically rigorous steps: comprehensive data compilation, meticulous data preprocessing, standardized data normalization, innovative feature engineering, and selective feature reduction. These steps prepare the dataset for effective machine learning model training and validation. We employ Z-score normalization to standardize numerical inputs, enhancing model convergence and performance. Recursive Feature Elimination (RFE) is utilized to identify and retain the most predictive features, streamlining the modeling process. A Random Forest Classifier, selected for its robustness and ability to handle nonlinear relationships, is trained to categorize air quality into predefined classes based on Air Quality Index (AQI) levels. This model not only quantifies but also scales air pollution intensity across different geographic and temporal scales, providing a tool for policymakers, environmental agencies, and the public to understand and mitigate the effects of air pollution. Through this framework, we demonstrate the application of machine learning in environmental science, highlighting its potential to transform air quality monitoring and management. Our approach is validated with a case study, showing promising results in predicting and scaling air pollution levels with high accuracy. This work lays the groundwork for future research and development in the field, suggesting directions for integrating more dynamic data sources and employing advanced machine learning algorithms to further enhance the predictive capability and scalability of air pollution models.