<p>The integration of artificial intelligence techniques into the cryptocurrency domain has attracted increasing scholarly attention, reflecting the broader shift toward data-driven innovation in the knowledge economy. In response to this burgeoning interest, this study presents a systematic literature review and bibliometric analysis to understand the landscape of AI applications within the realm of cryptocurrencies, following systematic review standards, Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for Scoping Reviews. Our results identify a significant increase in interest in this intersection since 2019, with China, the USA, and India emerging as leading countries driving research efforts. International collaboration among researchers is extensive, underscoring the global effort in this field. The research landscape is rapidly diversifying, with a focus on leveraging machine learning (ML) models like LSTMs, CNNs, and Transformers for various applications such as price prediction and trading strategies. Additionally, promising techniques like graph neural networks and multimodal fusion methods are being explored for anomaly detection and more accurate forecasting. Automated machine learning methods, including neural architecture search and ensemble techniques, are increasingly utilized to optimize cryptocurrency analytics. The paper also emphasizes the importance of exploring emerging digital assets like non-fungible tokens (NFTs) and stablecoins, suggesting future research directions that align with the innovation-driven dynamics of the knowledge economy. Overall, the findings emphasize the transformative potential of AI in redefining cryptocurrency analytics and decision-making, and call for continued interdisciplinary research to support sustainable growth in this rapidly evolving digital ecosystem.</p>