Abstract The rapid advancement of digital technologies has created significant opportunities but has also exposed vulnerabilities, leading to an increase in sophisticated cyber threats. This study explores the application of Artificial Intelligence (AI) and Machine Learning (ML) in cybersecurity as a response to these challenges. The research identifies key gaps in traditional cybersecurity measures, including limitations in detecting and responding to evolving threats through batch-patch resources. Using a literature review and simulation-based methodology, scholarly evidence was synthesized to analyze the effectiveness of AI and ML tools. The findings reveal that AI-powered solutions can enhance threat detection, automate responses, and improve predictive capabilities using Large Action Models (LAMs), which bridge understanding with action by executing tasks through system-level operations. However, challenges such as data privacy concerns, adversarial AI, explainability, and resource constraints persist. The study recommends investing in workforce training, improving adversarial resilience, and adopting adaptive cyber-defense frameworks. This manuscript concludes that AI and ML hold transformative potential in cybersecurity, but their adoption requires a balanced approach supported by measurable evaluation metrics, adaptive vulnerability management, and continuous model improvement (Buczak & Guven, 2016; Goodfellow et al., 2015; Malkawi & Alhajj, 2026; Saxe & Berlin, 2015; Zhang et al., 2025).