This article examines how artificial-intelligence technologies can improve the efficiency of distributed computing systems that face challenges of scalability, overload and limited flexibility in responding to external changes. The aim of the study is to explore AI-based methods designed to increase performance in distributed environments. The research draws on a theoretical analysis of publications in the field of distributed computing. Machine-learning algorithms allow forthcoming load changes to be detected in advance and computing tasks to be reassigned automatically, thereby reducing response time and boosting overall performance. Employing neural networks to analyse utilisation and redistribute resources improves the operation of distributed systems and smooths peak-load periods. The findings will be of interest to professionals working with distributed computing systems, cloud technologies and to other researchers investigating methods for enhancing the reliability and performance of computing platforms. The study concludes that integrating artificial intelligence into distributed systems increases their efficiency and resilience, opening new opportunities for optimising modern computing infrastructures.

artificial intelligence, distributed systems, machine learning, neural networks

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