The goal of this study is to analyze the role of artificial intelligence (AI) in cybersecurity by analyzing the strengths of many key AI techniques, including machine learning (ML), deep learning (DL), natural language processing (NLP), and anomaly detection algorithms. We conducted a detailed literature assessment on current research to assess the applicability of AI approaches in cybersecurity. Principal methodologies examined include machine learning for behavioral analytics, deep learning for malware detection, natural language processing for phishing identification, and anomaly detection for intrusion detection. Artificial intelligence approaches significantly enhance cybersecurity, with each strategy providing distinct functionalities. This study’s distinctive contributions are its comparative assessment of approaches, emphasizing their strengths and practical applications in cybersecurity while addressing present issues and future avenues for improvement. The research suggests that a combination of AI in cybersecurity that merges these strategies could provide a significant increase in digital security. These findings bring substantial value by identifying the best scenarios for using each AI approach in cybersecurity.