| Machine learning (ML) and artificial intelligence (AI) are evolving fast as disruptive technologies in cancer diagnosis and therapy, providing the ability to not only detect the disease at its earliest stage but also optimize treatment based on the patient's specific needs. Radiology and digital pathology Advanced deep learning architectures allow the classification, segmentation, and prognostication with high accuracy, which often significantly exceeds traditional diagnostics. Parallel developments in multi-omics integration and in biomarker discovery have facilitated non-invasive tumor subtyping and patient stratification to form the basis of precision oncology. Algorithms that utilize AI to plan therapies and predict survival and toxicities further add to the personalization of the treatment, but the generalizability of clinical applications was likely to be limited by a lack of prospective validation and the diversity of available data. Privacy-preserving multi-institutional collaboration has been proposed with federated learning structures, and interpretability techniques are enhancing clinician trust with biologically meaningful explanations. Data harmonization, subgroup fairness, regulatory compliance, and sustainable post-deployment monitoring are persistent challenges. The economic analyses explain that cost-effectiveness and viability of reimbursement are needed to facilitate the adoption of the technology in various health systems. The next steps will be based on future clinical implementation, regulatory harmonization, and an equity-first construct to make sure that technical advances lead to quantifiable patient value. By closing the gap between diagnostic accuracy, predicting response to therapy, privacy, fairness, and implementation science, AI has the potential to provide a holistic system to tackle the complexity of cancer care, and challenges continue to shape the path to safe and sustainable application to clinical practice.