The integration of deep learning and secure data handling has become critical in modern Smart Healthcare systems. This paper proposes a comprehensive hospital management framework for brain tumor detection that ensures both diagnostic accuracy and data security. The system comprises three key components: (1) a secure, Windows-based healthcare interface enabling remote access to patient data; (2) a convolutional neural network (CNN)-driven model optimized for automated brain tumor classification from MRI scans; and (3) a dual-layer cryptographic mechanism combining AES-128 encryption and Bcrypt hashing to preserve data confidentiality and integrity during storage and transmission. The proposed approach is benchmarked against existing state-of-the-art models, demonstrating competitive performance in terms of classification accuracy, computational efficiency, and resistance to security breaches. The system’s capability to deliver precise tumor detection while maintaining stringent privacy protocols underlines its potential for clinical adoption in high-stakes diagnostic environments.