Impact and Implementation of Artificial Intelligence in Central Bank Digital Currencies: A Systematic Literature Review

Rapidly transforming financial tools, central bank digital currencies (CBDCs) owe much to artificial intelligence (AI) for their development and application. By addressing three main questions – the documented effects of AI on CBDCs, the prevalent AI technologies and methods used, and the implementation strategies alongside related challenges – this systematic literature review seeks to comprehensively examine the junction of AI and CBDCs. Following strict inclusion criteria, 93 peer-reviewed publications published between 2017 and 2025 were carefully chosen using Scopus and Web of Science databases. NVivo tools allowed thematic coding and synthesis of the material, so facilitating an in-depth qualitative analysis. This method guaranteed a complex knowledge of AI’s several functions inside CBDC systems. The results show that AI greatly improves risk management, financial forecasting, regulatory compliance, and user CBDC interaction. The field is dominated by key AI technologies like random forest algorithms, support vector machines (SVM), transformer models, and long short-term memory (LSTM) networks because of their interpretability and predictive accuracy. Promising directions are shown by implementation strategies, including hybrid AI-blockchain frameworks, edge computing, and reinforcement learning; but, data privacy, technical integration, regulatory uncertainty, and ethical issues present obvious difficulties. Emphasizing the need of a complete 102 integration of AI technologies combined with strong governance structures, this study offers a disciplined road map for legislators and researchers. The insights provide vital direction for future research and practical implementation in the changing digital currency scene, so helping to advance the knowledge of AI’s transforming power in forming safe, efficient, and user-centric CBDCs.