Metabolism is fundamental to cellular function, supporting macromolecule synthesis, signaling, growth, and cell-cell communication. While single-cell and spatial metabolomics technologies have advanced, large-scale applications remain challenging. In contrast, transcriptomics provides vast datasets to infer metabolic states. Here, we present scCellFie, a computational tool that predicts metabolic activities from transcriptomic data at single-cell and spatial resolutions. scCellFie enables scalable analysis of large cell atlases, leverages metabolic tasks for interpretable results, and includes modules for identifying metabolic markers, condition-specific changes, and cell-cell communication. We applied scCellFie to ~30 million human cells, generating a comprehensive metabolic atlas across organs while demonstrating our tool’s scalability. Additionally, we used scCellFie to study the human endometrium, the uterine lining that undergoes substantial remodeling throughout the menstrual cycle due to sex hormones, and identified cell type-specific metabolic programs supporting cyclical changes. Epithelial cells exhibited metabolic regulation covering pathways supporting proliferation and mitigating oxidative stress. Endometrial diseases, including endometriosis and endometrial carcinoma, often arise from metabolic dysregulation. By inspecting eutopic endometrium from donors with endometriosis, we identified altered metabolic programs that likely drive atypical proliferation and inflammation of the distinct cell types. In endometrial carcinoma, malignant cells displayed metabolic rewiring, including increased glucose-to-lactate conversion and dysregulated kynurenine and estrogen signaling. These shifts suggest shared mechanisms promoting aberrant proliferation and may reveal therapeutic targets. Together, our findings demonstrate scCellFie as a scalable, interpretable tool for characterizing metabolism in health and disease. By linking metabolic functions to cellular processes, scCellFie provides deeper insights into metabolic regulation across diverse biological systems.