<p>Breast cancer (BC), a leading cause of cancer mortality in women, involves complex molecular mechanisms including post-translational modifications (PTMs). While common PTMs are well-studied, lactylation—a novel PTM linking metabolism to epigenetics—has emerged as a critical regulator in BC pathogenesis. The high concentration of lactate caused by the Warburg effect makes lactylation in tumor tissue possible. Machine learning enhances the discovery and analysis of lactylation sites by processing large datasets, revealing patterns that influence tumor behavior and patient outcomes. This review offers a comprehensive analysis of the role of lactylation in breast cancer development and progression. It focuses on the impact on the proliferation, migration, and invasion of breast cancer cells and their association with chemotherapeutic resistance. This review provides valuable insights for the development of targeted therapeutic strategies that may enhance early diagnosis, treatment, and prognostic evaluation of breast cancer. The findings presented here open new avenues for future research and clinical practice.</p>