A Unified Machine Learning Framework for Predictive Analytics across Cloud and Edge Computing Environments

  • Authors

    • Bhuvaneshwari Department of Computer Science, Holy Cross College (Autonomous), India. Author

    DOI:

    https://doi.org/10.67228/30713315/IJAIDT-V9I2P101

    Published 04-06-2026

  • Machine Learning, Predictive Analytics, Cloud Computing, Edge Computing, Distributed Intelligence, Federated Learning, Model Optimization, Internet of Things, Adaptive Inference, Resource Orchestration

    Issue

    Vol. 9 No. 2 (2026)

    Section

    Articles

  • Abstract

    As organizations increasingly adopt cloud-based systems, there is a growing need for efficient predictive analytics frameworks that integrate cloud and edge computing. However, existing solutions face challenges related to model compatibility, resource allocation, communication latency, data privacy, and prediction consistency. This paper proposes a unified machine learning framework integrating data preprocessing, distributed model training, dynamic inference placement, model synchronization, performance monitoring, and privacy management. The framework dynamically allocates tasks based on latency requirements, computational capacity, energy consumption, network availability, and data sensitivity. A multidimensional evaluation approach considers prediction accuracy, inference latency, bandwidth usage, energy efficiency, scalability, and reliability. As a conceptual study, the framework presents analytical expectations and testable hypotheses rather than fabricated experimental results. The proposed approach provides a foundation for intelligent applications in healthcare, smart manufacturing, transportation, financial services, and industrial IoT. By coordinating task placement, model synchronization, and resource management, the framework aims to improve the adaptability and efficiency of cloud-edge predictive analytics.

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