This research presents a composite approach to bank security issues by integrating the Internet of Things (IoT) threat reduction and artificial intelligence (AI) into systems. In this research, machine learning (ML) techniques are deployed across key banking operations such as ATM false alarms, health monitoring of hardware components, and adding security to locker rooms. Through the utilization of ML models such as CNN, SVM, KNN, and RNN, significant advancements are achieved in detecting fraudulent activities within ATM transactions. ML-driven approaches have high precision, recall and accuracy rates in bolstering ATM network security. In addition, predictive maintenance models trained on sensor data from ATM hardware components enable proactive maintenance interventions. This in turn leads to improved ATM availability, reduced downtime, and cost savings. Biometric authentication systems based on ML models appear to have promising effects in locker room security. It is aimed at enabling security measures to be improved intelligently. Overall, this research shows how banks can use AI to tackle threats such as those maxing home machines or video-recordings by infiltrating introductory courses. By making the most of IoT-linked data and ML algorithms, the ability of banks to detect and prevent fraudulent activities can be improved in addition to having a preventive maintenance program where fees are paid in ATM withdrawals. These advancements contribute not only to safeguarding the financial assets and customer data but also to fostering trust and confidence in the banking systems. Continual research and innovation in AI and IoT technologies are crucial in order to strengthen banking security measures against the digital challenges.