The development of medical-imaging neurology diagnostics regarding brain tumor detection and classification via the Internet of Medical Things (IoMT) is important. This research proposes a comprehensive framework addressing user privacy concerns by embedding brain tumor information in a cover image through Hybrid Watermarking Steganography (HWS) using Compressive Sensing Integer Wavelet Transform (CSIWT). The watermarked images are securely transmitted over the IoMT, ensuring data integrity. An Inverse CSIWT-HWS system extracts the hidden brain tumor image for diagnosis. The proposed framework incorporates an Optimized Brain Tumor Segmentation and Classification Network (OBTSC-Net) to enhance diagnostic capabilities. This transfer learning model utilizes Attention Generative Adversarial Networks (AGAN) to segment brain tumor areas from the extracted images, Hybrid Greylag Goose Optimization Genetic Algorithm (HGGO-GA) for disease-specific feature extraction from segmented images, and Broad Learning System Neural Network (BLS-NN) for the accurate classification of benign and malignant brain tumors using BraTS-2020 and BraTS-2021 datasets, offering a reliable and secure tool for remote diagnosis. Finally, the proposed HWS-CSIWT method achieved an average Peak Signal-to-Noise Ratio (PSNR) improvement of 12.65% over existing state-of-the-art methods. The proposed AGAN method achieved an average segmentation accuracy (SACC) improvement of 5.63% over existing methods, and the proposed OBTSC-Net achieved an average classification accuracy (CACC) improvement of 2.82% over existing state-of-the-art methods, confirming its enhanced diagnostic capability in brain tumor classification.