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This paper presents a comprehensive overview of how artificial intelligence (AI) is revolutionizing diagnostic imaging through advanced machine learning and deep learning techniques. It explores the fundamental principles behind AI innovations—including traditional methods like Support Vector Machines and Random Forests, as well as deep learning models such as convolutional neural networks and transformer-based architectures—and their applications in detecting, classifying, and segmenting medical images. The discussion extends to the critical role of data curation, performance evaluation, and emerging strategies like transfer learning and multi-task learning in enhancing model robustness and generalizability. In addition, the paper reviews AI applications across various imaging modalities, including radiography, CT, MRI, ultrasound, and nuclear medicine, while highlighting key clinical tasks and use cases such as automated detection, segmentation, diagnosis, and workflow optimization. Finally, it examines the technical, operational, and regulatory challenges associated with integrating AI into clinical workflows, emphasizing the need for rigorous validation, compliance with international and national standards, and transparent risk management. Together, these insights underscore AI’s transformative potential to improve diagnostic accuracy, streamline clinical decision-making, and ultimately enhance patient outcomes.