In the field of medicine, anomalous pathology prediction has become a major issue. Huma, instrument/device, and environmental errors have all contributed to the growth of these issues; yet, they can all be rectified with the use of the hybrid segmentation method. The main objective of this paper is to present a novel method, named as MS-IT2FCM, which targets the erroneous brain tumor diagnosis of abnormalities in many topographical Magnetic Resonance Imaging (MRI) regions. In the proposed method, we utilize the features of the Monkey Search algorithm and the Interval Type-II Fuzzy C-Means (IT2FCM) techniques. The uncertainties in the data are handled with interval type-II fuzzy numbers and search algorithm are used to optimize the results. Large datasets and the complex tumors can be easily examined and intervened upon by the developed method. Additionally, this could be a proactive measure implemented in clinical practice to benefit patients and physicians. The proposed methodology is implemented on the data set of BRATS 2018 and compare their results with the state-of-art. From the analysis, we conclude the results are better than those of the standard strategy in terms of predicting different diseases in MR brain imaging. The proposed method offers a clear distinction between the tumor and non-tumor portions (edema) and this clause may be included in medical pre-planning at all times. © 2025 Elsevier B.V., All rights reserved.