A Systematic Review of Artificial Intelligence Techniques for Distributed Storage and Data Processing in Cloud Computing

Distributed storage and data processing systems are now widely used due to the increasing amount of data produced by contemporary software. Conventional methods depend on rule - driven procedures as well as fixed algorithms that frequently do not adjust to changing workload and diverse surroundings. In order to optimize system performance in immediate circumstances, artificial intelligence provides data - driven and adaptable methods. Hence, AI - based techniques have drawn greater interest for enhancing distributed systems' effectiveness, dependability and performance by facilitating intelligent decision making. The industry and academic studies for artificial intelligence have expanded significantly in the past few decades, and with numerous possible breakthroughs and improvements in this area, this development is anticipated to accelerate. Previously developed systems have issues with latency, reliability and ineffective use of resources in unpredictable tasks, emphasizing the requirement for flexible and adaptable alternatives. Therefore, this paper systematically examined the existing artificial intelligence paradigms for distributed storage and data processing in cloud computing. The articles published from 2020 to 2025 were discovered, examined and evaluated. The comparison of existing studies demonstrates notable gains in storage usage, reduction of latency, minimization of operational expenses and rapid computation. This study shows how AI-based systems can improve distributed data storage and processing in the cloud. This evaluation provides a succinct manual for experts and academics negotiating the difficult environment of AI - driven platforms for data processing and distributed storage, along with recommendations for further study.