Money laundering remains a significant global challenge, posing threats to economic stability and security. With advancementsin artificial intelligence (AI) and machine learning (ML), financial institutions can enhance their anti-money laundering (AML) strategies.Traditional rule-based systems often generate excessive false positives and fail to adapt to evolving laundering techniques, making theminefficient. This paper presents a novel AI-driven framework for detecting suspicious activities, integrating time-frequency analysis,machine learning algorithms, and real-time detection systems. The proposed model utilizes Random Forest and Support Vector Machines(SVM) for accurate classification, reducing false positives and improving detection accuracy. By applying Fast Fourier Transform (FFT)for feature extraction, the system captures complex transactional patterns that remain undetected in conventional systems.Furthermore,the framework is designed for scalability and real-time implementation, ensuring rapid identification of suspicious behavior. Financialinstitutions can leverage this approach to mitigate financial crime risks, reduce operational costs, and enhance regulatory compliance.The model’s adaptability allows it to respond to emerging money laundering methods, including structuring, smurfing, and trade-basedlaundering. Additionally, the system provides actionable insights by analyzing patterns across multiple financial networks, contributingto the identification of criminal networks and illicit fund flows.Experimental results indicate a substantial reduction in false positive ratesand a notable increase in detection accuracy compared to traditional AML approaches. Moreover, with continuous model refinementusing real-time data, the system evolves to detect emerging threats effectively. By bridging the gap between regulatory requirements andtechnological advancements, this research contributes to the development of robust, scalable, and secure AML solutions. The proposedframework lays a strong foundation for further advancements in financial crime detection and global financial integrity protection. Keywords: Money Laundering, Anti-Money Laundering (AML), Financial Crime Detection, Time-Frequency Analysis, Fast FourierTransform (FFT), Financial Networks, Criminal Network Analysis, Scalable AI Solutions