This work explores the application of AI-driven cloud automation for removing latency from distributed systems based on the simulation of diverse workloads over the Alibaba Cluster Trace Datasets. Latency reduction in cloud systems is a metric with obvious implications for end-system and system performance. With the increasing use of cloud computing, the reduction of latency becomes the need for the optimal provision of resources and enabling task scheduling with the least interruptions necessary. The technique analyzes the fine-grained traces of the data for network latency, resource consumption, and task scheduling, and utilizes machine learning for latency prediction and reduction. Most striking are the dramatic reductions in task execution times and the increase in resource consumption efficiency, leading to system performance enhancement. This study concludes that AI-driven solutions can substantially decrease latency in cloud systems, and with further enhancement, it has potential for applications involving big data distributed systems.