Precision therapeutics are taking a new form due to the intersection of nanomedicine and artificial intelligence. Although effective in the delivery of drugs into the specific target, the traditional system of nanomedicine delivery is known to be characterized by problems of interpatient variation, inappropriate dosage, and unpredictability of treatment effects. This paper examines how machine learning algorithms can be implemented with patient specific data to create and optimize custom nanomedicine delivery platforms. Predictive models can be established using genomic, proteomic, and clinical data to inform the formulation of nanoparticles, predict the biodistribution of these nanoparticles, and reduce the side effects. The suggested model focuses on a data-informed pipeline that customizes the properties of nanocarriers, i.e., size, surface chemistry, and release rate, to the profile of specific patients. Case reports and new uses draw attention to the translational opportunities of this methodology in cancer, metabolic diseases, and the treatment of chronic diseases. Although each area incurs certain challenges, such as maintaining quality of the data, ethical issues, and regulatory avenues, the transformation of nanomedicine delivery through machine learning-based personalization is an essential step to precision healthcare. In this paper, the authors highlight the importance of interdisciplinary innovation in increasing the rate of clinical acceptance of personalized nanotherapeutics.