Cancer remains a leading cause of mortality worldwide. Patient-derived organoids (PDOs) are three-dimensional (3D) cultures that recapitulate tumor histology, genetics, and cellular heterogeneity, providing physiologically relevant preclinical models. Integrating PDOs with artificial intelligence (AI) and machine learning (ML) enables scalable analysis of high-dimensional datasets, including imaging, transcriptomics, proteomics, and pharmacological readouts. These approaches support prediction of drug sensitivity, biomarker discovery, and patient stratification. Recent advances—such as deep learning (DL), transfer learning, federated learning, and self-supervised learning—enhance phenotypic profiling, cross-institutional model training, and translational prediction. In this review, we summarize the current state of AI-driven PDO research, highlighting methodological approaches, preclinical and clinical applications, challenges, and emerging trends. We also propose strategies for standardization, validation, and multi-modal integration to accelerate patient-specific therapeutic strategies.