This paper provides an updated review of the research trends in using deep learning and machine learning (ML/DL) for cloud computing security. The authors identify 4051 publications in the Scopus database up to December 2023, highlighting key solutions such as anomaly detection, security automation, and the role of emerging technologies. However, challenges like data privacy, scalability, and explainability are also discussed. The paper emphasizes that ML and DL are emerging research areas in cloud security, with future research directions focusing on addressing these challenges and developing algorithms that comply with relevant laws and regulations. The introduction highlights the growing threat landscape, citing studies from the SANS Institute, Ponemon Institute, Cybersecurity Ventures, and others, which underscore the importance of advanced security measures. Traditional security methods like firewalls and IDS are limited in cloud environments, making ML and DL-based systems a promising solution for enhancing security detection rates and reducing false positives.This paper provides an updated review of the research trends in using deep learning and machine learning (ML/DL) for cloud computing security. The authors identify 4051 publications in the Scopus database up to December 2023, highlighting key solutions such as anomaly detection, security automation, and the role of emerging technologies. However, challenges like data privacy, scalability, and explainability are also discussed. The paper emphasizes that ML and DL are emerging research areas in cloud security, with future research directions focusing on addressing these challenges and developing algorithms that comply with relevant laws and regulations. The introduction highlights the growing threat landscape, citing studies from the SANS Institute, Ponemon Institute, Cybersecurity Ventures, and others, which underscore the importance of advanced security measures. Traditional security methods like firewalls and IDS are limited in cloud environments, making ML and DL-based systems a promising solution for enhancing security detection rates and reducing false positives.