Distributed Systems Analysis for Artificial Intelligence in Cloud Computing: A Comprehensive Review of AI-Based Applications and Services

Abstract

Objective: This study aims to examine the development and application of Artificial Intelligence (AI) in distributed cloud environments and identify emerging technologies, research trends, benefits, challenges, and future directions of AI-driven cloud services.
Research Design & Methods: A literature review was conducted using the Scopus database. The search focused on publications related to Artificial Intelligence, Distributed Cloud, Cloud Environment, and Applications and Services.
Findings: The results indicate a growing research interest in AI-enabled distributed cloud environments during the last five years. The literature reveals three major technological pillars: Optimization-Based AI, Machine Learning-Based AI, and Deep Learning-Based AI. AI has been widely adopted for resource management, workload scheduling, anomaly detection, cybersecurity, and autonomous cloud operations. Emerging trends include cloud-native architectures, multi-cloud systems, edge-cloud computing, Explainable AI (XAI), federated learning, and multi-agent reinforcement learning. However, challenges remain regarding infrastructure heterogeneity, scalability, security, explainability, and the generalization capability of AI models across diverse cloud environments.
Implications: The findings highlight the importance of integrating intelligent and adaptive AI mechanisms to improve efficiency, security, scalability, and operational automation in distributed cloud infrastructures.
Contribution & Value Added: This study provides a comprehensive synthesis of recent AI developments in distributed cloud environments and proposes a structured classification of AI approaches. The results offer insights into current research gaps and future opportunities for developing autonomous, trustworthy, and sustainable AI-driven cloud ecosystems.