Data analytics and data processing are continuously increasing in cloud applications. They should face the challenges of data scalability, smart, and stable workflow management. The existing data processing applications are not scalable and fault tolerant. Hence, this research aims to provide an adaptive workflow orchestration framework that can manage the entire workflow automatically, with improved scalability, and handle a large volume of data using a directed acyclic graph available in Apache Airflow. The proposed framework can handle multidimensional and multilevel data processing flow in any distributed environment. The framework monitors continuously in a real-time environment, and it uses effective and dynamic scheduling, dynamic prioritization, and confirms the data flow even in inconsistent workloads. To do this, a machine learning algorithm is implemented to predict the availability of requested resources, bottlenecks in resource allocation, and the execution path to ensure automatic data processing in the cloud. It also reduces the overall execution time and functional overhead problems. The machine learning algorithm learns the historical data efficiently to make accurate decisions to reduce pipeline letdowns. To demonstrate the overall process and performance of the framework, the Kubernetes, BigQuery, Amazon S3, and native services are integrated to provide improved reliability and scalability when deploying the framework processes in multi- and hybridcloud architectures. It also includes the checkpoints and recovery points that provide robust fault management in the entire architecture. From the experimental outputs, the proposed orchestration frameworks outperform the others in terms of throughput, latency reduction, scheduling, fault-tolerance, and cost. This research also highlights that the integration of automations, intelligence, and adaptability in the cloud workflow framework and its potential value can be used in the next generation of cloud data processing applications.