Internal audit, as one of the main pillars of monitoring the financial and operational processes of organizations, plays a vital role in identifying and mitigating risks. In this regard, the use of modern artificial intelligence and machine learning techniques can help improve the accuracy and efficiency of these processes and transform the internal audit cycle into a smarter and more efficient tool.In this paper, we examine and validate the internal audit cycle based on machine learning and artificial intelligence techniques. The main goal of this research is to design and evaluate a model that, using machine learning and artificial intelligence techniques, helps improve the internal audit process and increase itThis research was conducted in 1403 using a survey of 384 experts in the fields of internal audit and information technology. The results of the research showed that the use of artificial intelligence and machine learning in the internal audit cycle has created a fundamental transformation in increasing accuracy, transparency, detecting fraud, reducing costs, and optimizing financial decisions.

Also, the validation of the model was examined through composite reliability, convergent validity, and divergent validity indices, and the results indicated high validity and reliability of the presented model.

Keywords: Internal Audit, Artificial Intelligence, Machine Learning, Structural Equation Modeling, Information Technology.s accuracy and efficiency in identifying internal risks and weaknesses.