Background: Although breast density is an important determinant of breast cancer risk and the likelihood of masking pathological lesions on mammography, it is insufficient to fully characterize the breast imaging phenotype on its own. Age, body mass index (BMI), total body fat percentage, breast size, compressed breast thickness, tissue distribution, and reproductive history influence not only the biological composition of the breast but also the technical conditions of mammographic imaging. Aim: To synthesize the evidence on how age, BMI, body composition, breast-specific anthropometric characteristics, reproductive factors, and population-specific characteristics influence mammographic imaging. Materials and Methods: In this review, we synthesized evidence from studies indexed in PubMed/MEDLINE, Scopus, Embase, and Web of Science from 2000 through August 2026, focusing on the associations of these factors with mammographic appearance, lesion conspicuity, BI-RADS classification, image quality, radiation dose, and screening performance. Result: Breast density generally decreases with advancing age and during the menopausal transition. Increasing BMI and adipose tissue volume are also associated with lower breast tissue density. Therefore, the absolute volume of fibroglandular and connective tissue should not be inferred directly from percent breast density alone. Breast-specific parameters, particularly compressed breast thickness and breast volume, are clinically important because they influence positioning, compression, exposure parameters, and mean glandular dose. Studies in Asian cohorts have demonstrated significant associations between age, body size, and mammographic characteristics, although substantial inter-population differences have been reported. Advances in digital breast tomosynthesis (DBT) and artificial intelligence (AI) are enabling a transition from single-category breast density assessment toward multidimensional breast phenotyping. Future research should integrate DICOM technical parameters, standardized anthropometric measurements, quantitative breast density measures, and externally validated AI-derived features to improve personalized screening and radiation dose optimization. Conclusion Breast imaging phenotype should be considered a multidimensional construct integrating breast density with age, body composition, breast spatial architecture, imaging parameters, and validated AI-derived features. This approach may improve biological interpretation, radiation dose estimation, and risk stratification while avoiding the oversimplification that a single breast-density percentage or BI-RADS density category can fully characterize the breast phenotype.