In the modern IT environment, business processes are being automated with the help of AI/ML models. In a world where businesses are confronted with complex challenges and fluctuating environments, it is imperative to incorporate AI to improve performance, reduce potential hazards, and promote sustainable development for companies with practical and strong business process management. In this paper, we demonstrate the application of Anomaly Detection using a multi-level Business Process Orchestration framework while considering the current state of technology and the business environment. In our framework, AI/ML and deep learning models help manage the interconnected (BOs) business and their sub-business objects (sub-BOs), which have different lifecycles. The specialised algorithm helps with accurate time tracking and detection of anomalies, such as delays or errors and inefficiencies, thereby enabling effective management of issues and improving the overall robustness of the processes. Some of the key activities of this approach are data mining, data cleaning, industry-specific model training, real-time analysis and the implementation of analytics and alert systems. This paper outlines the idea and implementation strategy, presents use cases, and discusses the substantial benefits of leveraging AI to streamline and optimise business processes. It will help the businesses ensure that operational integrity and responsiveness standards are well met, as threats should be identified and addressed before they become crises.