Breast cancer, the most prevalent malignant tumor among women globally, presents a substantial clinical challenge owing to its extreme heterogeneity and treatment resistance. Nanomedicine, characterized by precise targeting, controlled release, and multi-mechanism synergy, offers innovative strategies to surmount the limitations of conventional therapies. This review systematically discusses recent advancements and clinical translation pathways of intelligent nanodrugs for breast cancer treatment. It begins with the conundrum of molecular subtype-specific treatments, focusing on the design principles of various delivery systems such as liposomes, polymeric nanocarriers, and inorganic nanoparticles. These systems have applications in enhancing tumor accumulation, reversing multidrug resistance, inhibiting metastasis, and regulating the immune microenvironment. The paper particularly highlights stimulus-responsive drug release, receptor-targeting strategies, and multi-modal synergistic therapy. It also critically examines bottleneck issues related to scaled production, immunogenicity, and individualized adaptation of nanodrugs during the transition from laboratory to clinic. A notable feature of this review is the integration of cutting-edge basic research and clinical trial data. For precision breast cancer therapy, we propose an AI-multi-omics integrated intelligent delivery paradigm: a random forest regression model (trained on 1,243 breast cancer patients’ genomics/metabolomics data) predicts optimal nanocarrier parameters—e.g., trastuzumab ligand density (20% vs. 50% conjugation) for HER2-positive tumors with high vs. low HER2 amplification, and glutaminase inhibitor release kinetics for TNBC with glutamine addiction. This model boosts the concordance between drug release and tumor proliferation peaks by 2.8-fold in Luminal B tumors (p < 0.001) vs. static carriers. This provides a theoretical foundation and practical roadmap for advancing the precision treatment paradigms in breast cancer, thereby holding significant academic reference and clinical guidance value.