Clinical image segmentation plays an important role in the diagnosis and treatment of brain tumours because it can identify the pathological area. This study aims to achieve accurate brain segmentation in magnetic resonance imaging (MR) images by combining ResNet50 architecture with entropy techniques. The methodology, which joins the best aftereffects of ResNet50 with entropy-based information content assessment, further develops the division exactness and eventually builds the dependability of emotional well-being analysis and treatment. This hybrid strategy aims to improve the accuracy and dependability of MRI image tumor segmentation. To start the review, X-ray pictures were pre-handled to work on their quality as contribution to the ResNet50 model. ResNet50’s use of deep learning improves its ability to read hierarchical features, allowing it to more precisely delineate tumor boundaries. The proposed strategy reliably outflanks the current technique, exhibiting its predominance and effectiveness in accomplishing exact and solid outcomes in terrifically significant estimations. Consolidating ResNet50 with entropy-based techniques for cerebrum division in MR pictures furnishes a compelling strategy with further developed exactness and unwavering quality. Good segmentation results are achieved by combining ResNet50’s deep learning capabilities with entropy testing’s numerical precision. The combination of these advancements increments exactness as well as gives consistency in the recognition and show of cerebrum growths, making it valuable in field clinical focuses.