The application of radiomics and AI to molecular imaging for prostate cancer (PCa) is reviewed, highlighting the potential of these technologies in improving diagnosis, staging, treatment planning, and outcome prediction. Molecular imaging, particularly PSMA-based PET/CT, plays a crucial role in PCa management. AI, including radiomics, convolutional neural networks (CNNs), generative adversarial networks (GANs), and unsupervised/semi-supervised learning, is increasingly used to enhance the accuracy and efficiency of imaging analysis. Radiomics involves extracting quantitative features from medical images to identify patterns that may correlate with clinical outcomes. CNNs are used for lesion detection, staging, and classification, while GANs are applied for image synthesis and segmentation. Unsupervised and semi-supervised learning methods help in utilizing large datasets without extensive labeling. Challenges include data scarcity, model reproducibility, and the need for larger, diverse datasets. The review emphasizes the potential of AI to improve PCa imaging, but also highlights the need for further research to address current limitations and ensure clinical applicability.The application of radiomics and AI to molecular imaging for prostate cancer (PCa) is reviewed, highlighting the potential of these technologies in improving diagnosis, staging, treatment planning, and outcome prediction. Molecular imaging, particularly PSMA-based PET/CT, plays a crucial role in PCa management. AI, including radiomics, convolutional neural networks (CNNs), generative adversarial networks (GANs), and unsupervised/semi-supervised learning, is increasingly used to enhance the accuracy and efficiency of imaging analysis. Radiomics involves extracting quantitative features from medical images to identify patterns that may correlate with clinical outcomes. CNNs are used for lesion detection, staging, and classification, while GANs are applied for image synthesis and segmentation. Unsupervised and semi-supervised learning methods help in utilizing large datasets without extensive labeling. Challenges include data scarcity, model reproducibility, and the need for larger, diverse datasets. The review emphasizes the potential of AI to improve PCa imaging, but also highlights the need for further research to address current limitations and ensure clinical applicability.