The security of Industrial Internet of Things (IIoT) systems is becoming a major problem as companies use IoT technology more and more to improve automation, productivity, and efficiency. The incorporation of Internet of Things devices into industrial environments presents novel avenues for assault and susceptibilities, rendering these systems appealing to malevolent entities. Attacks by malware on IIoT systems can have serious repercussions, such as data breaches, production interruptions, and possible harm to physical infrastructure. Effectively anticipating and averting malware assaults on IIoT systems is a problem. Conventional IIoT security systems frequently depend on signature-based detection techniques, which compare known malware signatures in order to locate and stop attacks. Nevertheless, the capacity of this method to identify novel and unidentified malware variants is restricted. Furthermore, signature-based systems' static nature can make it difficult for them to adapt to the complex and dynamic IIoT contexts. Furthermore, the distinct features of the Internet of Things, such the need for real-time operation and resource limitations, contribute to the difficulty of putting in place efficient security measures without compromising system performance. In light of this, this research creates a clever and inventive malware prediction system for IIoT. The suggested paradigm holds relevance due to its capacity to offer security measures that are both proactive and adaptable. The system may uncover abnormalities suggestive of possible malware activity by analyzing device behavior and network traffic in real-time through the use of sophisticated machine learning and classification algorithms. The use of a proactive strategy minimizes the likelihood of industrial processes being disrupted and ensures the integrity and security of sensitive data by enabling the early detection and mitigation of risks