This paper proposes an integrated cybersecurity approach leveraging advanced artificial intelligence (AI) techniques for the detection of rootkits in malware. Rootkits pose significant threats to the security of computer systems, as they are designed to conceal their presence and compromise the integrity of operating systems. The proposed approach combines traditional security measures, such as signature-based detection and behavioral analysis, with cutting-edge AI methodologies to enhance the efficiency and accuracy of rootkit detection. The integrated approach encompasses machine learning algorithms trained on diverse datasets of known rootkits and benign software, enabling the system to distinguish between malicious and nonmalicious behavior. Behavioral analysis continuously monitors system activities for anomalies, while memory analysis and file integrity monitoring provide insights into potential intrusions at the core level. Additionally, sandboxing and network traffic analysis are employed to simulate and observe the behavior of suspicious files and detect malicious patterns in network communication. Continuous monitoring, user and entity behavior analytics (UEBA), and collaborative threat intelligence further fortify the system against evolving threats. The paper emphasizes the importance of a holistic cybersecurity strategy, combining multiple layers of defense mechanisms, to ensure comprehensive protection against rootkit infections. The proposed approach is adaptable and aims to stay ahead of emerging threats, providing a robust defense mechanism for contemporary computing environments.