The combination of blockchain technology and Deep Convolutional Neural Networks (DCNN) provides a novel approach to address critical challenges in decentralizing healthcare systems, particularly in ensuring the secure and transparent handling of information. Current medical facilities often face issues related to information security, patient confidentiality, and the effective management of large, complex datasets. Blockchain offers a secure, tamper-resistant framework for distributed information transfer and storage, while Convolutional Neural Networks (CNNs) excel at analyzing vast amounts of unstructured health data, including patient records and medical images. Problems with existing systems include data insecurity, lack of transparency, and difficulty in safeguarding patient confidentiality when healthcare providers share information. This study proposes a methodology that integrates blockchain technology with deep learning to tackle these challenges. The proposed approach ensures that sensitive health information is securely stored on a blockchain while deep learning processes the data to generate accurate diagnoses and predictions. The primary goals of this research are to enhance data privacy, build trust among healthcare stakeholders, and improve the accuracy of medical predictions. The results of the proposed model show significant improvements in information privacy, accountability, and the predictive accuracy of medical data, contributing to better patient care and increased confidence in decentralized healthcare environments.