This article presents an analysis of frameworks designed for AI-driven orchestration of cloud resources, focusing on contemporary methods and architectural models aimed at improving the efficiency, adaptability, and energy performance of cloud computing environments. The study includes a comprehensive review of applied machine learning techniques, deep learning, reinforcement learning algorithms, evolutionary algorithms, and hybrid approaches used for workload prediction, resource allocation optimization, and autonomous decision-making. The paper identifies key integration challenges, computational overhead, issues of interpretability and security, and outlines development prospects through the implementation of Explainable AI and standardized modular architectures. The findings demonstrate the potential of the proposed approaches for practical implementation in dynamic cloud infrastructures. The insights provided in this article will be of interest to researchers and professionals working in the fields of distributed computing, cloud technologies, and artificial intelligence, as it analyzes modern frameworks designed to build efficient coordination systems within hybrid computing environments. Moreover, the material will be useful for specialists and academics seeking to integrate cutting-edge technological solutions into corporate and research projects, enabling optimized data processing and enhanced adaptability of information systems in an era of continuous digital transformation.

cloud computing, orchestration, artificial intelligence, machine learning, deep learning, reinforcement learning, evolutionary algorithms, optimization, predictive analytics, hybrid methods

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