Cyber-Physical Systems (CPS) are becoming increasingly integrated into various critical sectors, including healthcare, transportation, and industrial automation. As these systems evolve, the need for robust security mechanisms becomes ever more pressing. Anomaly detection has emerged as a crucial technique for identifying malicious activities and potential failures in these systems. This paper explores the key anomaly detection techniques used to secure CPS, emphasizing statistical, machine learning, and deep learning approaches. The review highlights their applications, strengths, challenges, and discusses potential future directions to enhance the security of CPS in the face of evolving cyber threats.