(untitled) (International Journal of Engineering & Extended Technologies Research (IJEETR), 2025)
The rapid evolution of enterprise digital infrastructures has accelerated the need for intelligent, autonomous, and secure DevOps frameworks capable of managing multi-cloud Enterprise Resource Planning (ERP) environments. This paper proposes an AI-driven Autonomous DevOps Framework for the seamless integration of SAP S/4HANA within a multi-cloud architecture, enhanced by the Apache open-source ecosystem and Wireless Sensor Networks (WSNs). The proposed framework leverages machine learning, predictive analytics, and reinforcement learning to automate configuration management, performance optimization, and anomaly detection across distributed cloud systems. By incorporating sensor-driven data streams into ERP workflows, the system enables real-time monitoring of physical and operational parameters, enhancing supply chain visibility and adaptive decision-making. The Apache stack (Kafka, Spark, Airflow) serves as the data orchestration backbone, ensuring scalable, low-latency data processing and secure inter-service communication. The architecture adopts a Zero-Trust Security Model and integrates AI-based continuous testing pipelines within DevSecOps for compliance, resilience, and cost optimization across multi-cloud deployments. This research contributes a unified model that bridges ERP automation, AI-based DevOps, and IoT-driven intelligence, fostering digital transformation for enterprises seeking efficiency, agility, and predictive operational control. Performance simulations and prototype evaluations demonstrate enhanced deployment velocity, reduced operational costs, and improved system resilience, positioning the framework as a cornerstone for next-generation enterprise automation.