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Background Breast cancer continues to be the leading global health concern and cause of cancer-related deaths among women, with about 20 million new cases and 9.7 million deaths estimated globally based on GLOBOCAN 2022 data. Breast cancer exhibits unique epidemiological patterns and heterogeneity that influence several characteristic features including tumor development, metastatic potential, treatment response, and outcomes. The knowledge of the complex interplay between genetic, environmental, and microenvironmental factors is critical for developing better diagnostic and therapeutic approaches. Methods A comprehensive literature search was carried out to review and compile the progress made in the field of breast cancer epidemiology, molecular subtypes, pathogenesis, diagnostic tools, and treatment strategies. The focus was on genetic susceptibility, hormonal and behavioral risk factors, tumor microenvironment, novel biomarkers, novel imaging and liquid biopsy techniques, and novel treatment strategies, besides recent advances in bioinformatics and AI-based analyses. Results Breast cancer can be broadly classified into major molecular subtypes i.e., triple-negative breast cancers, Luminal A, Luminal B, and HER2-enriched. Each subtype exhibits unique biological behavior and therapeutic vulnerabilities. Tumor growth is driven by intricate interactions between the tumor microenvironment (TME) and immune cells, inflammatory mediators, extracellular matrix remodeling, hypoxia, cancer stemness, cellular senescence, and metabolic dysregulation. Progress in diagnostics have been made, with the integration of molecular characterization, genomics, and non-invasive liquid biopsies, in addition to traditional imaging and histopathology. Therapeutic modalities have expanded beyond conventional approaches such as surgery, chemotherapy, and radiation to modern day approaches such as targeted therapies, antibody-drug conjugates, immunotherapy, plant and nanomedicine-based therapies, and new cellular therapies. Also, computational biology and artificial intelligence (AI)-based approaches are now rapidly increasing for biomarker identification, treatment decision-making, and patient outcome prediction. Conclusion In summary, the current review provides an updated and comprehensive prospective in complex nature of breast cancer development, diagnostic approaches, and treatment options.