Image registration is a cornerstone in fields like medical imaging, remote sensing, and computer vision, facilitating comparative analysis and data fusion through alignment to a unified coordinate system. This research explores the integration of rigid and non-rigid image registration techniques in the context of Web 6.0 interfaces, focusing on applications in medical imaging and object recognition. Rigid registration, characterized by affine transformations and ORB feature extraction, offers computational efficiency for scenarios with minimal deformations. Non-rigid registration, leveraging iterative transformation models, accommodates complex changes like stretching and warping. The study employs advanced models to align RGB images from video sequences and evaluates registration quality using metrics such as Pearson Correlation, Mean Squared Error (MSE), and Mean Joint Entropy (MJE). Results indicate that non-rigid methods consistently surpass rigid techniques in achieving higher correlation, lower MSE, and reduced MJE. These findings underscore non-rigid methods’ flexibility and precision, highlighting a trade-off between computational efficiency and accuracy, with implications for the development of dynamic, user-centric Web 6.0 interfaces.