Artificial intelligence (AI) is transforming medical imaging by improving diagnostic accuracies, diminishing observer variability, and enhancing workflow efficiency. This review explores the advancement of AI-imaging techniques across modality-lines mammography, ultrasound, magnetic resonance imaging (MRI), computerized tomography (CT), and digital pathology. Several studies demonstrate the potential for AI to assist radiologists in detecting tumors, classifying them and diagnosing neurodegenerative disorders: often outperforming the traditional method to compare it to. Deep learning algorithms and other AI models have shown the potential to automate image segmentation, extract features and perform predictive analysis that would enable early diagnosis and personalized treatment. However, challenges remain due to variability in the training data, validation constraints, and the need for integration into clinical workflow. This review also highlights the potential of AI in medical imaging and discuss future perspectives toward its implementation for better patient outcomes.