The cyber security industry is witnessing a significant shift as more and more companies look to artificial intelligence (AI) to rethink their defense strategies against emerging cyber threats. This study explores how artificial intelligence (AI) is fundamentally changing the way that threat detection paradigms are traditionally understood. Through an examination of the past and present of traditional approaches, we draw attention to the growing necessity of artificial intelligence-driven solutions. The paper provides a thorough examination of the basis of artificial intelligence (AI) in threat detection, with a focus on neural networks and machine learning methods. This paper examines several AI-powered threat detection methods, such as behavioral analysis, anomaly detection, and a comparison of heuristic-based and signature-based strategies. Examined are issues like explain ability, interpretability, and adversarial attacks, which offer a thorough understanding of the difficulties and factors, related to AI-driven cyber security. With a focus on threat intelligence platforms, explainable AI, predictive analytics, and integration, the paper takes a forward-looking approach to improving cyber security. The effectiveness and efficiency of AI-powered threat detection are compared to more conventional techniques in a comparative analysis that ends with advice for businesses looking to use AI-driven solutions. Key findings and their implications for the changing cyber defense landscape are summarized in the study&#39, s conclusion. By doing this research, we hope to add to the continuing conversation about the development of threat detection and provide useful advice and insights to help enterprises successfully incorporate AI into their cyber security plans.