The rapid adoption of cloud computing has transformed the way organizations manage and store their data. However, this shift has also increased vulnerabilities to cyber threats. Anomaly detection is a critical component of cybersecurity frameworks, allowing for the identification of unusual patterns that may indicate security breaches. This paper presents a comprehensive framework for anomaly detection in cloud computing environments. It reviews existing methodologies, explores the integration of machine learning techniques, and discusses the challenges associated with implementing these systems. The proposed framework aims to enhance the cybersecurity posture of organizations by providing proactive detection of anomalies.