The proliferation of Internet of Things (IoT) devices has led to an increased risk of IoT botnet malware attacks. These attacks exploit vulnerabilities in IoT devices, allowing malicious actors to compromise them and use them as part of a botnet for various malicious activities. This thesis focuses on the development and implementation of an intelligent framework for the detection and prevention of IoT botnet malware. The framework utilizes advanced machine learning techniques and intelligent algorithms to identify and mitigate the threats posed by IoT botnets, enhancing the security and resilience of IoT ecosystems.