The extension of conventional Internet of Things (IoT) technologies from basic applications to advanced industrial processes has revolutionized operational efficiency, giving rise to a new class of IoTs called the Industrial Internet of Things (IIoT) systems. Despite these advancements, critical IIoT infrastructures remain exposed to a growing range of cybersecurity threats due to open vulnerabilities, stemming from the fact that these IIoT networks try to interconnect physical machinery with digital systems. Cyberattacks targeting IIoT systems attempt to exploit these vulnerabilities in order to cause severe operational disruptions and costly outages. These intrusions pose significant risks to data integrity, operational continuity, and total production, highlighting the need for effective cybersecurity measures in the industrial setting. To help prevent such intrusions, this work proposes a unified predictive framework combining cyberattack detection with outage prediction to enhance resilience in IIoT environments. By leveraging machine learning algorithms, this framework analyses two types of data—network traffic for cyberthreats and historical sensor data for forecasting the Remaining Useful Life (RUL) of critical components. This approach aims to identify risks and send alerts to minimize operational downtime preemptively, integrating predictive techniques for both cybersecurity and system reliability to strengthen IIoT systems, thereby helping industries maintain continuous, secure, and safe operations.