Proficient log analysis plays a pivotal role in maintaining the dependability and operational efficiency of expansive cloud-hosted infrastructures. As the intricacy of these ecosystems increases, conventional monitoring techniques often fail to keep up, leading to heightened computational overhead and diminished efficiency. This study investigates a blended methodology that fuses natural language processing strategies with an enhanced AdaBoost classification framework to detect irregularities within log data produced by cloud platforms. Given that classification accuracy is highly dependent on precisely adjusted hyperparameters, the system employs a tailored adaptation of the well-known sinh cosh optimization algorithm to refine the parameter tuning phase. The fine-tuned AdaBoost variants were tested on openly accessible, real-world log repositories. Findings from the experiments reveal outstanding outcomes, with top-performing models surpassing 99.96% accuracy, highlighting the framework’s promise as a scalable, intelligent mechanism for anomaly identification in cloud logging environments.