A tumor signifies the uncontrolled growth of cancer cells in the body, and brain tumors significantly contribute to cancer-related deaths across all age groups. Timely detection of brain tumors is crucial to prevent further complications. Traditionally, a biopsy, often required invasive brain surgery, is conducted to determine the type of brain tumor. Healthcare professionals can harness computational intelligence techniques to assist in identifying and categorizing these tumors. The classification of brain tumors is pivotal for assessing and planning effective treatments. In the field of medical image processing, the detection and segmentation of brain tumors through MRI scans represent a central and challenging research area. The integration of Artificial Intelligence (AI), encompassing machine learning and deep learning, has transformed the categorization and detection of complex pathological conditions like brain tumors. Deep learning, in particular, excels in the precise segmentation and classification of these tumors, which is crucial for the analysis and interpretation of brain MR images. In this study, we introduce a hybrid RNN classifier, combining Inception ResNet and RNN, to enhance the classification of various brain tumor types. This innovative approach is expected to yield improved results in terms of precision, recall, and F1-score.