Deep learning and machine learning have shown effectiveness in identifying and addressing cloud security threats. Despite the large number of publications in this field, there is a lack of comprehensive reviews that synthesize techniques, trends, and challenges of using deep learning and machine learning for cloud computing security. This paper aims to provide the most updated statistics on the development and research in cloud computing security utilizing deep learning and machine learning. Up to the middle of December 2023, 4051 publications were identified after searching the Scopus database. The paper highlights key trend solutions for cloud computing security utilizing machine learning and deep learning, such as anomaly detection, security automation, and the role of emerging technologies. However, challenges such as data privacy, scalability, and explainability are also identified as challenges of using machine learning and deep learning for cloud security. The findings reveal that deep learning and machine learning for cloud computing security are emerging research areas. Future research directions may include addressing these challenges when utilizing machine learning and deep learning for cloud security. Additionally, exploring the development of algorithms and techniques that comply with relevant laws and regulations is essential for effective implementation in this domain. The SANS Institute found that 84% of effective cyberattacks exploit human behaviors. A Ponemon Institute study found that the average cost of a data leak caused by network failures is $4.1 million. Cybersecurity Ventures projects that the yearly cybercrime cost will exceed USD 7 trillion globally in 2022 and $10.5 trillion by 2025. Research by the Information Systems Security Association found that using various data sources could improve the precision of vulnerability management identification by up to 50%. The amount of sensitive information is anticipated to increase by 50% annually, according to a report by the International Association of Computer Science and Information Technology. Also, according to the same report, anomaly detection can identify up to 85% of breaches. Unfortunately, the same report claimed that adversarial cyberattacks can deceive Deep Learning (DL) models up to 90% of the time. On the other hand, using security intelligence may decrease the time necessary to find a security breach by up to 50%. According to the same research, the SANS Institute found that finding a security event takes an average of 200 days. Contemporary malware presents a significant challenge for traditional detection systems due to its sophisticated and deceptive nature. In cloud environments, antivirus programs often struggle to detect complex malware, such as encrypted or metamorphic variants, leading to an increased risk of undetected attacks. Despite their widespread use, traditional security methods like firewalls and Intrusion Detection Systems (IDS) have limitations in cloud settings. They cannot effectively identify novel threats, zero-day attacks, or malicious mining programs, nor can they handle large volumes of data. Consequently, there is a pressing need to ensure high detection rates with accuracy to reduce false positives and bolster security measures. One promising approachDeep learning and machine learning have shown effectiveness in identifying and addressing cloud security threats. Despite the large number of publications in this field, there is a lack of comprehensive reviews that synthesize techniques, trends, and challenges of using deep learning and machine learning for cloud computing security. This paper aims to provide the most updated statistics on the development and research in cloud computing security utilizing deep learning and machine learning. Up to the middle of December 2023, 4051 publications were identified after searching the Scopus database. The paper highlights key trend solutions for cloud computing security utilizing machine learning and deep learning, such as anomaly detection, security automation, and the role of emerging technologies. However, challenges such as data privacy, scalability, and explainability are also identified as challenges of using machine learning and deep learning for cloud security. The findings reveal that deep learning and machine learning for cloud computing security are emerging research areas. Future research directions may include addressing these challenges when utilizing machine learning and deep learning for cloud security. Additionally, exploring the development of algorithms and techniques that comply with relevant laws and regulations is essential for effective implementation in this domain. The SANS Institute found that 84% of effective cyberattacks exploit human behaviors. A Ponemon Institute study found that the average cost of a data leak caused by network failures is $4.1 million. Cybersecurity Ventures projects that the yearly cybercrime cost will exceed USD 7 trillion globally in 2022 and $10.5 trillion by 2025. Research by the Information Systems Security Association found that using various data sources could improve the precision of vulnerability management identification by up to 50%. The amount of sensitive information is anticipated to increase by 50% annually, according to a report by the International Association of Computer Science and Information Technology. Also, according to the same report, anomaly detection can identify up to 85% of breaches. Unfortunately, the same report claimed that adversarial cyberattacks can deceive Deep Learning (DL) models up to 90% of the time. On the other hand, using security intelligence may decrease the time necessary to find a security breach by up to 50%. According to the same research, the SANS Institute found that finding a security event takes an average of 200 days. Contemporary malware presents a significant challenge for traditional detection systems due to its sophisticated and deceptive nature. In cloud environments, antivirus programs often struggle to detect complex malware, such as encrypted or metamorphic variants, leading to an increased risk of undetected attacks. Despite their widespread use, traditional security methods like firewalls and Intrusion Detection Systems (IDS) have limitations in cloud settings. They cannot effectively identify novel threats, zero-day attacks, or malicious mining programs, nor can they handle large volumes of data. Consequently, there is a pressing need to ensure high detection rates with accuracy to reduce false positives and bolster security measures. One promising approach