Researchers have developed an artificial intelligence-driven vital signs monitoring system that demonstrates superior capability in detecting clinical deterioration within intensive care units compared to traditional fixed-threshold methods. Published in the International Journal of Ad Hoc and Ubiquitous Computing, the innovation addresses a longstanding limitation in hospital monitoring: conventional systems evaluate physiological metrics such as heart rate, blood pressure, and oxygen saturation in isolation, often missing complex cross-parameter interactions and patient-specific baselines. The new architecture integrates three distinct machine learning methodologies to overcome these constraints. An adaptive attention mechanism dynamically recalibrates the weighting of individual physiological signals based on real-time patient context. This is paired with a spatiotemporal graph neural network that maps interdependencies among vital signs and tracks their evolution over time. Finally, a reinforcement learning framework enables the system to evolve its decision-making protocols through continuous feedback, shifting its function from passive alarm generation to active clinical decision support. Validation testing utilized data from two prominent intensive care databases, MIMIC-III and eICU. The AI system achieved an anomaly-detection accuracy of 96.3 percent, generated predictive alerts approximately 40 minutes prior to critical medical events, and suppressed false alarm rates to 6.4 percent. These metrics indicate a substantial reduction in alert fatigue for medical staff while providing earlier intervention windows for deteriorating patients. By moving beyond static cutoff values and incorporating dynamic physiological relationships, the system represents a significant advancement in computational healthcare monitoring. Its deployment could streamline intensive care workflows, minimize delayed responses to acute complications, and ultimately improve patient survival rates through more precise and timely clinical interventions.