(untitled) (International Journal of Engineering & Extended Technologies Research (IJEETR), 2025)
The convergence of cloud computing, artificial intelligence (AI), and deep learning is transforming digital ecosystems across both healthcare and agriculture. This study presents an integrated framework that leverages cloud-based computational resources and SAP enterprise systems to enhance medical data management, equipment monitoring, and clinical decision support, while simultaneously enabling intelligent leaf disease threat detection in agricultural settings. The proposed architecture utilizes scalable cloud services to process large volumes of heterogeneous data, advanced AI algorithms for predictive analytics, and deep learning models for accurate disease identification and risk assessment. SAP integration ensures streamlined workflows, secure data handling, and seamless interoperability with organizational processes. By bridging two critical domains—healthcare management and crop protection—this unified system demonstrates how modern digital technologies can improve operational efficiency, strengthen early-warning capabilities, and support data-driven decision-making. The framework highlights the potential of hybrid cloud–AI infrastructures to address multidisciplinary challenges and deliver enhanced outcomes for both human health and agricultural sustainability.