Recent analysis published in the Journal of Medical Internet Research demonstrates how artificial intelligence is fundamentally reshaping radiopharmaceutical development and precision oncology. The review synthesizes emerging applications of deep learning and generative AI in nuclear medicine, detailing measurable improvements in drug discovery timelines and patient-specific treatment planning. Conventional radiopharmaceutical therapy development demands extensive time and financial resources. AI-driven computational frameworks are now streamlining this process by rapidly identifying novel therapeutic targets and predicting complex molecular interactions. Dr. Sofia Michopoulou of the University Hospital Southampton notes that machine learning simulations allow researchers to vet high-potential candidates earlier in the pipeline, significantly reducing preclinical experimentation and concentrating early-phase evaluations on the most viable options. Beyond initial discovery, AI is optimizing personalized dosimetry through advanced image analysis and predictive modeling. Three-dimensional convolutional neural networks process clinical imaging to forecast radiation biodistribution within human tissue. This capability enables the construction of patient-specific digital twins, granting oncologists the ability to simulate radiation exposure scenarios. Such precision maximizes therapeutic efficacy against tumors while minimizing collateral damage to healthy organs. Despite these technological strides, clinical implementation remains constrained by data infrastructure limitations. The radiopharmaceutical AI sector currently lacks standardized, high-fidelity datasets necessary for training reliable models. Federated learning architectures are being explored to enable multi-institutional collaboration while maintaining patient privacy. However, experts stress that extensive foundational experimental validation is still required to guarantee model generalizability and clinical safety. As data standardization improves and computational methodologies mature, AI-integrated radiopharmaceuticals are expected to accelerate the delivery of targeted cancer therapies and establish new benchmarks for individualized medical treatment.