As critical infrastructure in aviation and healthcare becomes increasingly complex, traditional reactive strategies for maintenance and security are proving insufficient for handling dynamic real-world environments. This research examines the integration of AI-enabled predictive monitoring and security frameworks to create resilient, self-sustaining systems that can manage uncertainty with minimal human intervention. Central to this transition is the application of AI and machine learning models—such as XGBoost, CNNs, and LSTMs—to move from scheduled to proactive maintenance by accurately predicting the Remaining Useful Life (RUL) of aircraft engines and providing early warnings for cardiac events in healthcare. Simultaneously, the study prioritizes security by developing defense mechanisms against cyber-physical threats, including GPS spoofing, ADS-B vulnerabilities, and unauthorized network intrusions across both aviation and smart airport infrastructures. Despite these advancements, significant barriers remain, including high computational overhead, a lack of model interpretability (the \\\"black box\\\" problem), and a gap between simulation and real-world deployment. This work concludes that the future of dependable infrastructure lies in unified, lightweight, and explainable frameworks that allow systems to autonomously detect threats, recover from faults, and maintain themselves in unpredictable conditions.