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Artificial intelligence (AI) has emerged as a transformative tool in nuclear imaging, offering innovative solutions to enhance diagnostic accuracy, image quality, and clinical workflows. This article explores the integration of AI methodologies, including deep learning and traditional machine learning models, into nuclear imaging modalities such as Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT). By leveraging advanced algorithms, AI addresses longstanding challenges in noise reduction, resolution enhancement, and tumor classification. Key findings demonstrate the application of AI in improving cancer detection, delineating tumor volumes, and predicting patient outcomes with unparalleled precision. The study highlights how convolutional neural networks (CNNs), generative adversarial networks (GANs), and vision transformers (ViTs) are revolutionizing medical imaging through superior image reconstruction and analysis capabilities. Limitations, such as the dependency on large, annotated datasets and ethical concerns, are discussed alongside future directions, including the integration of multi-modal imaging data and real-time AI applications. In conclusion, AI represents a paradigm shift in nuclear imaging, advancing personalized medicine and improving patient outcomes. However, further research is required to address challenges and unlock its full potential in clinical practice.