This study combines explainable machine learning with thermodynamic modeling to quantitatively assess how meteorological conditions and chemical composition jointly contribute to aerosol pH variation in an ammonia-rich urban atmosphere. The analysis highlights regime-dependent interactions, threshold behaviors, and sample-specific variability, providing a data-driven framework for interpreting aerosol acidity under diverse environmental conditions.