The Internet of Things and cloud computing have become pivotal technologies for modern society, transforming how social, advertising, and economic domains are impacted. From education to smart cities, IoT and cloud ecosystems show great potential in enhancing human life quality through robotics, better operational efficiency, and satisfaction across various industries. The fast integration of IoT devices and cloud platforms has introduced significant security risks and vulnerabilities, coming from sophisticated and evolving cyber threats. Conventional security mechanisms fail to address these challenges in most cases, leaving IoT and cloud infrastructures exposed to various attacks. Machine learning (ML) and deep learning (DL) have thus emerged as revolutionary solutions for the enhancement of IoT and cloud security in overcoming such limitations. With AI-driven technologies, ML and DL present dynamic, adaptive, and intelligent mechanisms for detecting, mitigating, and preventing threats by extracting actionable insights from massive volumes of raw data. It becomes possible to address advanced security challenges while ensuring system scalability and reliability by integrating ML and DL with IoT and cloud frameworks. This paper provides a systematic review of machine learning applications in IoT and cloud security, focusing on their role in mitigating cyberthreats and improving system resilience. We explore a range of ML and DL techniques, their implementation in real-world IoT-cloud scenarios, and their effectiveness in addressing emerging threats. Finally, we highlight critical research questions and propose future directions to advance security in IoT and cloud ecosystems using AI-driven approaches.