The rapid expansion of cloud computing has led to the continuous generation of massive system log data, making manual analysis difficult, time-consuming, and prone to errors [1][2][6][10]. This work proposes an LLM-based cloud log analyzer that automates the interpretation of logs and assists in identifying root causes using Artificial Intelligence. The system gathers logs from cloud platforms such as AWS CloudWatch and CloudTrail, processes them to extract meaningful attributes, and applies Large Language Models (LLMs) for efficient log analysis [1][2][3][4]. The proposed approach detects anomalies, recognizes patterns, and identifies root causes including permission-related issues, resource limitations, network configuration errors, and application-level failures [5][8][12][13]. In addition, it produces clear human-readable explanations and suggests automated corrective actions, thereby reducing reliance on domain experts and lowering system downtime [12][14]. A web-based dashboard is also implemented to present error summaries, root cause insights, and recommended solutions in an understandable format. By combining cloud computing with Generative AI, the system improves operational efficiency, strengthens cloud reliability, and supports the evolution of AIOps in modern IT environments [3][5][8][12].