This review explores the evolution of radiological imaging – from early X-rays to modern computed tomography, magnetic resonance imaging, ultrasound, and positron emission tomography – and the growing role of artificial intelligence in medical diagnostics. Focusing on head and neck cancer, parotid gland tumors, and thyroid nodules, it highlights how machine learning (ML) and deep learning (DL) techniques, including convolutional neural networks (CNNs), radiomics, and texture analysis, enhance tumor detection, classification, and prognosis. AI models often outperform traditional methods in accuracy and consistency, yet challenges persist. These include small and retrospective study cohorts, limited data standardization, and regulatory constraints under GDPR and MDR. The review emphasizes the importance of data interoperability via DICOM and HL7 standards to support broader AI adoption. Despite current limitations, artificial imaging holds transformative potential in radiological diagnostics. Its successful clinical integration depends on rigorous study design, transparent validation, and adherence to ethical and legal standards to ensure reliability, safety, and patient trust in AIdriven healthcare.