This review focuses on the application of artificial intelligence (AI) in molecular imaging for prostate cancer (PCa). It highlights the importance of magnetic resonance (MR) imaging and positron emission tomography with computed tomography (PET/CT), particularly Prostate-Specific Membrane Antigen (PSMA)-based PET/CT. The review aims to introduce AI technologies and their applications in PET/CT for PCa, including lesion detection, staging, treatment planning, and outcome prediction. Key AI methodologies discussed include radiomics, convolutional neural networks (CNNs), generative adversarial networks (GANs), and various training methods such as supervised, unsupervised, and semi-supervised learning. Radiomics involves analyzing detailed structural information from medical images to extract numerical features for statistical and AI analysis. CNNs are used for classification and segmentation tasks, often outperforming human radiologists in certain areas. GANs are employed for tasks like dosimetry prediction, tumor segmentation, and dose plan translation. The review also addresses challenges such as dataset size, imbalanced data, and explainability, and discusses the role of AI in supporting clinical decision-making, particularly in borderline cases. Despite these advancements, there is skepticism among stakeholders regarding the integration of AI into clinical practice, and regulatory frameworks and data ownership are important considerations for future development.This review focuses on the application of artificial intelligence (AI) in molecular imaging for prostate cancer (PCa). It highlights the importance of magnetic resonance (MR) imaging and positron emission tomography with computed tomography (PET/CT), particularly Prostate-Specific Membrane Antigen (PSMA)-based PET/CT. The review aims to introduce AI technologies and their applications in PET/CT for PCa, including lesion detection, staging, treatment planning, and outcome prediction. Key AI methodologies discussed include radiomics, convolutional neural networks (CNNs), generative adversarial networks (GANs), and various training methods such as supervised, unsupervised, and semi-supervised learning. Radiomics involves analyzing detailed structural information from medical images to extract numerical features for statistical and AI analysis. CNNs are used for classification and segmentation tasks, often outperforming human radiologists in certain areas. GANs are employed for tasks like dosimetry prediction, tumor segmentation, and dose plan translation. The review also addresses challenges such as dataset size, imbalanced data, and explainability, and discusses the role of AI in supporting clinical decision-making, particularly in borderline cases. Despite these advancements, there is skepticism among stakeholders regarding the integration of AI into clinical practice, and regulatory frameworks and data ownership are important considerations for future development.