The fusion of artificial intelligence with digital image correlation (DIC) is revolutionizing experimental mechanics by addressing long-standing challenges in accuracy, speed, and adaptability. Traditional DIC techniques, while effective in measuring full-field displacement and strain, struggle under conditions involving complex geometries, high deformation gradients, noise, and limited computational efficiency. Recent advancements leverage AI, particularly deep learning (DL) and machine learning (ML), to enhance various aspects of DIC, including speckle pattern evaluation, denoising, subset optimization, and strain prediction. Convolutional neural networks (CNN)-based models, such as DisplacementNet, StrainNet, and DICTr, enable accurate, real-time deformation measurement, often outperforming conventional methods under extreme or noisy experimental scenarios. Additionally, AI techniques facilitate automated subset selection, residual stress correction, and biomechanics-informed tissue strain tracking. The integration of AI not only enhances accuracy and robustness but also paves the way for physics-informed and interpretable DIC frameworks. These hybrid models embed domain knowledge into learning algorithms to enforce physical constraints and improve generalizability. Case studies in aerospace, biomechanics, and residual stress analysis illustrate AI’s practical benefits, achieving higher spatial resolution, better noise tolerance, and real-time applicability. Nonetheless, challenges persist, including the need for large, diverse training datasets, ensuring generalizability across materials and geometries, and improving model interpretability. Despite these limitations, AI-enhanced DIC offers a paradigm shift transforming DIC from a post-processing tool into a real-time, adaptive measurement system. This synergy has broad implications for experimental mechanics, digital twins, and smart manufacturing systems.