For patients with various types of cancer, including brain, breast, and lung cancers, medical imaging is the gold standard for early detection, diagnosis, therapy planning, monitoring, and image-guided interventions. To facilitate interoperability and integration with clinical, genomic, and proteomic data, the majority of images are digitally stored in the standardized Digital Imaging and Communications in Medicine (DICOM) format. Radiomics transforms standard-of-care medical images into high-dimensional quantitative features, capturing tumor shape, texture, intensity, and heterogeneity. Combining these characteristics with artificial intelligence (AI) and machine learning models, such as CNNs, DenseNet, EfficientNet, and hybrid ensemble approaches, allows for predictive modeling and personalized oncology. These features serve as reliable, non-invasive biomarkers for predicting cancer risk, diagnosis, prognosis, and treatment response. With increasing clinical validation across multiple cancer types, AI-driven radiomics enhances diagnostic accuracy, reproducibility, and scalability. However, challenges remain, including feature standardization, demographic biases, high computational requirements, and integration into clinical workflows. Consequently, to accelerate clinical acceptance, future directions include eXplainable AI models, standardized DICOM-compliant processes, and integration with multi-omics data. This review highlights the applications, advantages, challenges, limitations, and prospects of AI-driven radiomics in oncology, emphasizing their transformative potential for precision medicine.