Cloud computing has become a critical platform for data storage and online services, offering advantages such as scalability, cost efficiency, and high availability. However, it also presents significant security challenges, particularly with malware detection. Traditional malware detection methods struggle with modern threats like polymorphic and encrypted malware, which evade detection through obfuscation and dynamic behavior. Machine learning (ML) and deep learning (DL) techniques are increasingly used to enhance malware detection accuracy and reduce false positives. Cloud-based detection systems leverage the scalability and computational power of cloud environments to analyze malware samples more effectively. These systems use a combination of heuristics, deep learning, signatures, and behavior analysis to detect malware. Despite these advancements, challenges such as resource wastage, data control issues, and lack of real-time monitoring remain. The paper reviews recent advancements in malware detection in cloud environments, highlighting the effectiveness of ML and DL in improving detection rates. It also discusses the limitations of traditional methods and proposes innovative approaches for more accurate and efficient malware detection. The study emphasizes the importance of cloud-based solutions in protecting IoT and cyber-physical systems from cyber threats. The paper also explores various techniques for malware detection, including dynamic analysis, static analysis, and the use of machine learning models. It concludes that cloud computing offers a promising solution for malware detection, but further research is needed to address the challenges and improve the efficiency and accuracy of detection systems.Cloud computing has become a critical platform for data storage and online services, offering advantages such as scalability, cost efficiency, and high availability. However, it also presents significant security challenges, particularly with malware detection. Traditional malware detection methods struggle with modern threats like polymorphic and encrypted malware, which evade detection through obfuscation and dynamic behavior. Machine learning (ML) and deep learning (DL) techniques are increasingly used to enhance malware detection accuracy and reduce false positives. Cloud-based detection systems leverage the scalability and computational power of cloud environments to analyze malware samples more effectively. These systems use a combination of heuristics, deep learning, signatures, and behavior analysis to detect malware. Despite these advancements, challenges such as resource wastage, data control issues, and lack of real-time monitoring remain. The paper reviews recent advancements in malware detection in cloud environments, highlighting the effectiveness of ML and DL in improving detection rates. It also discusses the limitations of traditional methods and proposes innovative approaches for more accurate and efficient malware detection. The study emphasizes the importance of cloud-based solutions in protecting IoT and cyber-physical systems from cyber threats. The paper also explores various techniques for malware detection, including dynamic analysis, static analysis, and the use of machine learning models. It concludes that cloud computing offers a promising solution for malware detection, but further research is needed to address the challenges and improve the efficiency and accuracy of detection systems.