This study examines the extent to which artificial intelligence (AI) enhances air-defence systems in countering modern air threats. Addressing the research niche at the intersection of algorithmic autonomy and time-critical defence decision-making, the study aims to assess whether mission-tailored AI pipelines, integrated across sensing, fusion, tracking, and command layers, improve detection latency, classification accuracy, trajectory prediction, and engagement success while reducing false alarms. The research is structured around testable sub-hypotheses (H1–H4). The study employs a modelling and simulation approach supported by a critical literature review. Scenario-based simulations were used to evaluate the impact of AI integration in selected air-defence systems protecting critical infrastructure. The results indicate substantial performance improvements. For example, integration of AI into the Patriot PAC-3 reduced mean target-detection time from 18 to 4 s and increased classification accuracy from 72% to 94%. Signal-processing throughput increased from 1200 to 8400 signals per minute, while reaction times (e.g., NASAMS) decreased from 35 to 8 s and interception success rose from 65% to 91%. For IRIS-T SLM, trajectory-prediction error decreased from 430 m to 55 m, and computation time from 7.0 to 1.5 s. The proportion of autonomous decisions increased from 25% to 80%, while false-alarm rates declined by 78.6%. These findings, derived from validated simulation scenarios, demonstrate that AI significantly improves adaptability, precision, and response speed in high-tempo environments. However, effective implementation requires strengthened cybersecurity, rigorous model validation, human-in-the-loop governance, and updates to regulatory frameworks to ensure safety, accountability, and interoperability.