<p>Radiogenomics seamlessly integrates radiological imaging phenotypes with molecular and cellular data, offering a powerful, noninvasive means to decipher the underlying biology of cancer. In recent years, artificial intelligence (AI), including machine learning and deep learning approaches, has revolutionized radiogenomics by automating the extraction of high-dimensional quantitative imaging features and enabling their robust correlation with multiomics profiles. This narrative review summarizes current advances in AI-powered radiogenomics, focusing on clinical relevance, molecular and cellular insights, and laboratory-based diagnostic implications guided by the principles of clinical chemistry. We explore the historical evolution from traditional imaging to data-driven, multiomics integration frameworks and highlight the rapidly growing application of AI methods (e.g., convolutional neural networks, generative adversarial networks, transformers) for feature extraction and integrative modeling and detail use cases across major cancer types, such as breast, lung, brain, and prostate cancer. By leveraging evidence from the latest peer-reviewed studies and open-access multi-institutional consortia, we illustrate how AI-enabled radiogenomics facilitates the discovery of imaging surrogates for genomic alterations, tumor heterogeneity, and the tumor microenvironment. Challenges, including data harmonization, standardization, ethical considerations, and validation across populations, are critically examined. Finally, we discuss future trends such as spatial transcriptomics integration, federated learning, and multiomics AI models, highlighting the transformative potential of radiogenomics in precision oncology and laboratory workflows. A growing body of evidence indicates that AI-powered radiogenomics holds promise in noninvasive biomarker discovery, therapy response prediction, and real-time disease monitoring, paving the way for individualized cancer management.</p>