Breast Cancer (BC) continues to be a predominant cause of cancer-related death globally, with clinical therapy impeded by off-target toxicity and limited therapeutic windows of traditional chemotherapeutics, especially across various molecular subtypes. This study critically assesses the integration of advanced nanocarrier engineering, Artificial Intelligence (AI), and personalized medicine in developing smart Polymeric-lipid nanoparticles (PLNs) for BC treatment. Preclinical data suggest that carefully designed PLNs with ligand-functionalized active targeting and stimuli-responsive release mechanisms may overcome biological barriers, improve tumor-specific accumulation, and potentially reduce systemic side effects compared with traditional formulations. By accelerating predictions of nanoparticle (NP)-bio interactions, optimizing formulation parameters (like lipid-polymer ratios and drug loading), and predicting in vivo performance before benchtop synthesis, we highlight the revolutionary potential of machine learning (ML) models in nanotherapeutic development. Additionally, including tumor microenvironment characteristics and patient-specific omics profiles may make it easier to strategically create customized PLN regimens that match the range of BC subtypes. AI-driven, patient-specific PLNs could increase anticancer efficacy through precision targeting while potentially lowering off-target toxicities, thereby expanding the safety margin, according to current preclinical evidence. However, clinical validation is still pending because no PLN formulation has entered registered clinical trials for BC. This paper presents a strategic approach to address key translational issues, such as scalable production and regulatory clearance. It argues that, with effective clinical translation and thorough prospective validation, combining nanotechnology and digital intelligence could radically change precision oncology for BC.
