This research examines how to improve supervisory effectiveness through AI functions in Iranian banking institutions using MAXQDA software. With the increasing complexity of banking operations and regulatory requirements, AI has emerged as an essential transformative technology that has great potential to enhance supervisory functions along with providing services and security measures. The research findings show that AI technologies including machine learning and natural language processing are able to accurately detect anomalies and fraud by monitoring large sets of financial data in real-time operations. This technology provides effective results through intelligent systems in three important areas including anti-money laundering (AML), financial advisory services, and improving customer experience with intelligent chatbots. Through these technologies, banks achieve higher operational efficiency, reduce human errors, and accelerate response times, which leads to increased customer trust and security. However, implementing AI in banking operations requires solving three fundamental challenges: transparency of system performance, data protection measures, and the development of appropriate legal frameworks to ensure privacy and security. This study, using qualitative methods and analyzing data collected in the software environment, examines the key variables affecting the supervisory performance of banks and provides operational solutions for the optimal and beneficial use of AI. The results of this study indicate that integrating AI into the Iranian banking system can lead to improved financial supervision, reduced risks, customer satisfaction, and the realization of smart and resilient banking.