(untitled) (International Journal of Engineering & Extended Technologies Research (IJEETR), 2025)
The convergence of enterprise resource planning (ERP), environmental intelligence, and healthcare analytics marks a new era in sustainable digital transformation. This research introduces a cognitive multi-cloud ERP architecture that integrates SAP S/4HANA, Apache frameworks, and IoT-based sensor networks to advance environmental and cancer health intelligence under an AI-governed cloud ecosystem. The proposed architecture leverages machine learning, distributed data orchestration, and real-time analytics to unify heterogeneous data from environmental sensors and clinical systems into a secure, scalable, and interoperable cloud environment. By embedding AI governance frameworks, the system ensures ethical data handling, policy-driven compliance, and adaptive risk mitigation across multi-cloud infrastructures. The integration of SAP’s ERP capabilities with Apache-based data pipelines enhances operational transparency and supports high-throughput data processing for predictive cancer detection and pollutant exposure analytics. A zero-trust security layer and LDDR (Low Data Duplication and Redundancy) optimization strategy are employed to improve data integrity and cost efficiency. Experimental evaluation demonstrates improved accuracy in environmental–health correlation models, lower latency in multi-cloud synchronization, and stronger policy enforcement through AI-governed control loops. This study establishes a cognitive foundation for sustainable, intelligent health ecosystems, bridging ERP, AI, and environmental monitoring within a unified cloud architecture.