This paper proposes a hybrid approach based on quantum variation classifier combined with classical machine learning algorithms to assess and classify air quality. Air quality data based on carbon dioxide, particulate matter, nitrogen dioxide and ozone concentrations were artificially generated to build a model for categorizing them into “Good”, “Moderate”, and “Poor” classes. The primary correlations between the variables were examined and visualized in two dimensions using principal component analysis. The quantum variation classifier model was contrasted with classical models like Random Forest and Logistic regression. The accuracy and confusion matrices were used to assess each model's efficacy. Results show that quantum models represent a promising direction for environmental monitoring and can compete with classical models.