New financial innovations and formidable regulatory hurdles, especially with regard to Anti-Money Laundering (AML) compliance, have emerged with the advent of Bitcoin. The decentralised, anonymous, and ever-changing nature of blockchain transactions makes traditional rule-based anti-money laundering solutions sluggish. This study offers a new framework that uses Reinforcement Learning (RL) to ensure that cryptocurrency transactions are always compliant with anti-money-laundering regulations. Robot learning agents may learn the best ways to detect, report, and flag suspicious actions in real-time by modelling anti-money laundering compliance as a sequential decision-making process. In order to identify developing patterns of illegal activity including structure, layering, and integration, the suggested RL model communicates with a virtual blockchain setting. The agent adapts its rules on the fly according to transaction details, user behaviour, and compliance results using a combination of continuous learning and incentive feedback. The system's capacity to adapt allows it to surpass static rule-based methods, particularly when it comes to identifying new methods of laundering and reducing the number of false positives. Furthermore, the framework uses explainable RL approaches to make compliance judgements more transparent and easier to understand, which is important for regulators to approve the system. A combination of supervised pretraining with labelled transaction data and temporal-difference learning allows the system to optimise policies to a finer degree. Additionally, it incorporates a compliance risk score system to prioritise transaction review according to changing behavioural risk instead of fixed criteria. Initial experimental findings show that as compared to traditional AML infrastructure, the RL-based method considerably enhances detection precision and flexibility. In addition to improving productivity, it optimises compliance procedures and decreases the need for human interventions. An intelligent, scalable compliance network that can respond to changing crypto dangers is possible, according to this study, and reinforcement learning may help with both ensuring compliance with AML and this endeavour. This work adds to the expanding body of research in artificial intelligence-driven regulatory technology (RegTech) by providing a thoughtful and proactive strategy for protecting the honesty of decentralised financial transactions. Connectivity with cross-chain compliance standards, real-world deployment issues, and multi-agent cooperation will be the topics of future study.