The emergence of Central Bank Digital Currencies (CBDCs) presents both opportunities and challenges for central banks worldwide. To ensure the successful implementation and operation of CBDCs, it is crucial to identify and mitigate potential risks. This paper proposes a deep learning-based model for assessing CBDC risk in central banks. Deep learning, a subset of artificial intelligence, has demonstrated remarkable capabilities in various domains, including image recognition, natural language processing, and fraud detection. Its ability to analyze complex patterns and extract meaningful features from large datasets makes it well-suited for the task of CBDC risk assessment. The proposed model aims to provide central banks with a robust and scalable tool to identify potential vulnerabilities, evaluate the likelihood of different risks, and suggest appropriate mitigation strategies. By leveraging deep learning techniques, the model can enhance the efficiency and accuracy of CBDC risk management processes. This paper will delve into the details of the proposed model, including its architecture, data requirements, implementation steps, and potential benefits. It will also discuss the challenges and limitations associated with using deep learning for CBDC risk assessment and explore potential future directions.