As the digital landscape evolves, so does the complexity and frequency of cyber threats, particularly code injection attacks. These sophisticated assaults exploit vulnerabilities within software applications to execute unauthorized commands, compromising security and privacy. This paper digs into the utilization of Artificial Intelligence (AI) techniques to strengthen defenses against such invasive attacks. Specifically, it explores the application of machine learning (ML) and deep learning (DL) models to detect and mitigate threats more effectively. The analysis includes a comparative study of various AI algorithms, assessing their efficacy in preempting cyber threats through past attack patterns. This research demonstrates the potential of AI-driven security solutions, proposing a structured approach to integrate these technologies within existing cybersecurity frameworks. The aim is to foster a more proactive, adaptive, and resilient cyber defense mechanism, capable of countering code injection attacks. The findings advocate for the ongoing development of AI capabilities in cybersecurity, suggesting that these technologies are not merely supplementary but essential to the future of digital security.