This journal discusses how artificial intelligence and machine learning can be applied in the field of forensic accounting. It starts with the explanation of how these tools are used as the parts of the financial surveillance and fraud detection systems. It points out that the activities that these technologies are about to guide us on are not in the research of fraud but in preempt measures of ensuring that the fraud does not take place. Such discussions are grounded in the theoretical implications and arguments such as Technology Acceptance Model, the Innovation Diffusion Theory and a few ethics that provide discussions about diffusion, adoption and dilemmas that the automation may present to professional ethics and issues. The most important aspects of AI in analytics include; predictive analytics, neural networks, natural language process, and automated evidence documentation. They are contrasted in many dimensions, accuracy, speed, scalability, real-time tracking, etc. In contrast to other publications, the study focuses on real-world adoption, with case studies of Peru, Rwanda, Nigeria, Canada and Thailand. At the same time, there are adoption issues related to infrastructure, training, and regulation. There are also negative consequences of increasing reliance on automated systems, algorithmic bias, lack of data, security over data, uncertainty regarding legal admissibility, and, of course, the total absence of any legal framework. These issues deserve separate research. Right now, the most critical are the ethical and legal issues around defining the professional ethical boundaries, the absence of any legal framework, and the overemphasis on the autonomy of AI systems. The most exciting directions for research are the fusion of AI with blockchain, big data, and the Internet of Things, collaboration with other systems, and evolution on self-improving vectors. There are numerous and profound opportunities but success lies in responsible ethical adoption. Artificial intelligence is.