The proliferation of cryptocurrencies, notably Bitcoin, has brought about a paradigm shift in financial transactions, necessitating robust mechanisms for detecting fraudulent activities within the decentralized and anonymized cryptocurrency networks. In this paper, we explore applying advanced machine learning techniques, particularly Graph Attention Networks (GATs) and its variant, GAT-TAGNN, for fraud detection within the context of Bitcoin transactions using the Elliptic dataset. Through meticulous data preprocessing, model training, and evaluation, we demonstrate the effectiveness of these approaches in identifying suspicious transactions indicative of fraudulent behavior. Specifically, our Tag Graph Neural Network (TAGNN) achieved a remarkable 99.12% train accuracy with a train loss of 0.028. Our Graph Attention Network-TAGNN (GAT-TAGNN) attained a train accuracy of 98.29% with a train loss of 0.063. Leveraging the rich transactional data and leveraging advanced technologies like artificial intelligence and blockchain, our study contributes to enhancing the security and transparency of cryptocurrency transactions. We discuss the theoretical underpinnings, empirical analysis, and potential applications of GAT-based approaches, highlighting their efficacy in addressing emerging challenges in cryptocurrency fraud detection. Our findings, therefore, highlight the need for more detailed research and the collaboration of diverse disciplines in forging strong fraud detection frameworks to safeguard the integrity of crypto-based transactions and generate confidence in digital financial ecosystems.