In the rapidly evolving field of cloud computing, ensuring robust security mechanisms is crucial to protect sensitive data and maintain system integrity. This research explores the integration of random forests and K-means clustering algorithms for enhancing anomaly detection and response in cloud environments. Traditional security measures often struggle to keep pace with the dynamic and scalable nature of cloud infrastructures. Our approach leverages the strengths of random forests in classification tasks and K-means clustering for identifying patterns and anomalies within complex datasets. The random forest algorithm is employed to classify and predict potential security threats based on historical data, providing a reliable means of detecting known attack vectors. Meanwhile, K-means clustering is utilized to analyze system logs and network traffic, identifying anomalous patterns that may indicate previously unknown threats or irregular activities. The goal of merging these two approaches is to improve cloud security by making threat detection faster and more accurate. This integrated approach offers several advantages, including the ability to handle large-scale data, provide real-time analysis, and adapt to evolving threat landscapes. The effectiveness of the proposed model is evaluated through a series of experiments, demonstrating significant improvements in detecting and responding to security incidents. Our research indicates that a strong foundation for improving cloud security may be gained by combining random forests with K-means clustering. This, in turn, can lead to more secure and resilient cloud-based systems.