This research presents a pioneering investigation into the tangible outcomes of implementing an Artificial Intelligence (AI) driven network security strategy, with a specific emphasis on dynamic threat landscape adaptation and the integration of time series analysis algorithms. The study focuses on the innovative fusion of adaptive mechanisms to address the ever-evolving threat landscape, coupled with the application of the Autoregressive Integrated Moving Average (ARIMA) time series analysis algorithm. Real-world case studies are employed to provide concrete evidence of the efficacy of these strategies in fortifying network defenses and responding dynamically to cyber threats. Novelty is introduced through the unified integration of dynamic threat landscape adaptation mechanisms that continuously learn and evolve. The paper details adaptive access controls, showcasing how the security system dynamically adjusts permissions in real time to respond to emerging threats. Additionally, the application of the ARIMA time series analysis algorithm represents a pioneering contribution to the field of cybersecurity. By unveiling temporal patterns in security incidents, ARIMA adds a predictive element to network defense strategies, offering valuable insights into potential future threats and enabling a proactive response. The findings underscore the practical impact of the applied strategies, with real-world case studies demonstrating substantial improvements in threat detection rates, the effectiveness of adaptive responses, and the predictive capabilities facilitated by ARIMA. This research contributes to the advancement of AI in network security by providing tangible evidence of the innovative and effective nature of the integrated approach. The outcomes bridge the gap between theoretical concepts and practical applications, offering valuable insights for organizations seeking adaptive and predictive strategies to enhance their cybersecurity resilience in dynamic threat environments.