The rapid expansion of distributed data engineering ecosystems has significantly increased the complexity of real-time anomaly detection across cloud-native infrastructures, Internet of Things (IoT) platforms, enterprise analytics pipelines, and decentralized computational environments. Traditional anomaly detection techniques are increasingly ineffective in addressing high-volume, heterogeneous, and continuously evolving data streams generated by modern distributed architectures. This research proposes an intelligent framework for real-time anomaly detection in distributed data engineering systems by integrating machine learning, adaptive stream analytics, distributed event processing, security-driven architectures, and AI-assisted predictive modeling. The framework focuses on improving detection accuracy, reducing latency, minimizing false positives, and enhancing scalability across multi-source data environments. The study synthesizes recent developments in AI-driven monitoring, blockchain-enabled security, quantum-aware cybersecurity models, predictive maintenance systems, and scalable ETL infrastructures. A layered methodological architecture is introduced to support anomaly identification, automated classification, distributed coordination, and response optimization. The findings indicate that intelligent distributed anomaly detection systems significantly improve operational reliability, cyber resilience, and infrastructure scalability when compared to static rule-based monitoring models. Furthermore, the study identifies critical implementation challenges related to model drift, distributed synchronization, computational overhead, and data governance. The proposed framework contributes to research on intelligent distributed analytics by establishing a scalable, adaptive, and security-oriented anomaly detection paradigm suitable for contemporary enterprise and cloud environments.