This systematic review explores the integration of Artificial Intelligence (AI) in Anti-Money Laundering (AML) practices in Bangladesh, utilizing the PRISMA framework. The study analyzes the current state of AML, the impact of AI, and the roles of various stakeholders, including policymakers, practitioners, and researchers. It also examines the legal and regulatory landscape, focusing on risks, laws, and implications of AI in AML. The review synthesizes findings from diverse sources such as academic research, industry reports, and regulatory documents, including 299 research papers, 181 of which are recent studies. The study highlights the potential of AI in detecting and preventing money laundering, while also addressing challenges such as data privacy, model transparency, and regulatory compliance. It provides insights and recommendations for policymakers and practitioners to effectively leverage AI in AML efforts. The research underscores the importance of AI in enhancing the efficiency, accuracy, and agility of AML processes, while ensuring compliance with legal frameworks and protecting privacy rights. The study also discusses the role of government and regulatory bodies in AML, emphasizing the need for legislative enactment, regulatory oversight, and international cooperation. The findings contribute to the ongoing discourse on the role of technology in addressing financial crime challenges in Bangladesh and globally. The study identifies research gaps and suggests future directions, including the need for interdisciplinary collaboration, ongoing surveillance, and capacity building in AI for AML. The review concludes that AI integration is a promising solution for improving the identification and prevention of financial crimes in AML efforts.This systematic review explores the integration of Artificial Intelligence (AI) in Anti-Money Laundering (AML) practices in Bangladesh, utilizing the PRISMA framework. The study analyzes the current state of AML, the impact of AI, and the roles of various stakeholders, including policymakers, practitioners, and researchers. It also examines the legal and regulatory landscape, focusing on risks, laws, and implications of AI in AML. The review synthesizes findings from diverse sources such as academic research, industry reports, and regulatory documents, including 299 research papers, 181 of which are recent studies. The study highlights the potential of AI in detecting and preventing money laundering, while also addressing challenges such as data privacy, model transparency, and regulatory compliance. It provides insights and recommendations for policymakers and practitioners to effectively leverage AI in AML efforts. The research underscores the importance of AI in enhancing the efficiency, accuracy, and agility of AML processes, while ensuring compliance with legal frameworks and protecting privacy rights. The study also discusses the role of government and regulatory bodies in AML, emphasizing the need for legislative enactment, regulatory oversight, and international cooperation. The findings contribute to the ongoing discourse on the role of technology in addressing financial crime challenges in Bangladesh and globally. The study identifies research gaps and suggests future directions, including the need for interdisciplinary collaboration, ongoing surveillance, and capacity building in AI for AML. The review concludes that AI integration is a promising solution for improving the identification and prevention of financial crimes in AML efforts.