Journal of Applied Physical Science International

Cloud-native systems increasingly rely on microservices, container orchestration, distributed data pipelines, and continuous monitoring to support scalable and resilient software operations. However, the distribution of services across dynamic infrastructure environments creates persistent challenges in data flow management, latency control, security enforcement, anomaly detection, and resource utilisation. This study proposes an Intelligent Data Flow Modelling Framework for cloud-native systems engineering. The framework integrates Microservices Architecture, Secure Data Pipelines, and Artificial Intelligence Monitoring through an Intelligent Data Flow Management layer designed to improve Cloud-Native System Performance. A design science research approach was adopted to develop the framework as a conceptual engineering artefact. The proposed architecture consists of cloud-native infrastructure, intelligent data flow, secure pipeline, AI monitoring and analytics, and optimisation and decision layers. The framework incorporates mathematical components for anomaly detection, data flow efficiency, security risk assessment, and resource utilisation efficiency. Simulation-based analysis was used to illustrate the potential behaviour of the proposed framework under varying workload conditions. The results suggest that the integrated approach may support improved throughput, controlled latency, high availability, anomaly detection, and workload-aware resource use under the modelled scenarios. In addition, a structural validation model is presented to guide future empirical assessment of the relationships among architectural, security, monitoring, data flow, and performance constructs. The study contributes a unified conceptual framework for secure and intelligent management of data flows in cloud-native environments. The findings should be interpreted as simulation-based and illustrative and require further validation in real-world production systems.