Metaheuristics plays a crucial role in problem-solving, and most of them are energized by the accumulated wisdom of biological quirks. Worldwide, bosom illness affects more than 10% of women at some point in their lives. Breast MRI registration is a technique for matching pre- and post-contrast images for cancer classification and analysis. It is essential to do breast MRI registration in order to align MR images of pre- and post-contrast for the purposes of diagnosis and categorization of cancer type as benign or malignant using pharmacokinetic analysis. It is also extremely important to align photos that are going to be collected at a variety of time intervals so that the lesion can be isolated at small intervals. This method of registration is very helpful for monitoring the effectiveness of many different cancer treatments. The primary enlightenment of algorithms that are used for image registration has also shifted from a control point for semi-automated techniques to sophisticated voxel-based automated techniques that employ mutual information as a similarity measure. This transition occurred as image registration moved from a manual process to an automated one. In this study, we present an optimization method based on student psychology and adversarial learning (SPBO-OBL) to be used for breast MRI registration. Breast Magnetic Resonance Imaging (MRI) image registration using SPBO-OBL, a meta-heuristics-based optimization technique. The SPBO-OBL technique is then used to successfully register the images. We evaluate the performance of the SPBO-OBL-based registration method against the GTO, BBO-EL, and PSO methods. According to the findings, SPBO-OBL-based registration methods are superior to GTO, BBO-EL, and PSO-based registration methods when it comes to the registration of breast MRIs.