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Aging affects lung function, predisposing older adults to respiratory diseases; however, the cellular and molecular mechanisms of lung aging are not fully understood. Leveraging single-cell and spatial transcriptomics data from 184 and 70 lung parenchyma samples, respectively, we present an analytical platform to dissect the cell composition, gene expression modules, and regulatory changes linked to multiple hallmarks of lung aging. Our findings show cell type-specific age-association of senescence markers and a decline in alveolar cell proliferation, autocrine WNT signaling, and stemness indicators with advancing age. Analysis of myeloid cells reveals a global reduction in macrophage subsets and a surge in mitochondrial dysfunction and inflammatory signaling. In contrast, lung parenchyma T cells expand with age and exhibit heightened interferon gamma expression, cytotoxic activity, and exhaustion in older lung and blood samples, indicative of age-related immune dysfunction. Cell interaction and spatial analysis demonstrate aberrant myeloid-T cell cross-talk, leading to an increase in T cell chemotaxis and activation. Lastly, we use machine learning to predict lung biological age and identify putative biomarkers of lung aging and disease risk.