In today's interconnected world, the widespread adoption of Internet of Things (IoT) devices has brought forth a host of conveniences and opportunities. However, this technological revolution has also opened the door to a new breed of cyber threats, with attackers exploiting vulnerabilities in IoT devices to compromise user privacy, disrupt critical services, and wreak havoc. Traditional security measures have proven inadequate to combat the evolving complexity of these cyber-attacks, necessitating a more advanced and adaptive approach. This urgency has given rise to the development of a Machine Learning Model for Cyber Attack Detection and Classification in IoT Environments (ML-IoT-CD). In addition, the need for a robust cybersecurity solution in IoT environments has become paramount due to the increasing reliance on these devices for critical applications. Existing intrusion detection systems and conventional security measures often lack the scalability and agility needed to keep pace with rapidly evolving attack techniques. As a result, there is a pressing demand for an intelligent, automated, and proactive cyber defense mechanism capable of real-time detection and classification of emerging cyber threats. The ML-IoT-CD model aims to fulfill this need by harnessing the power of machine learning algorithms to analyze vast amounts of data generated by IoT devices. By doing so, it can effectively distinguish between legitimate and malicious activities, thereby bolstering the security posture of IoT ecosystems.