Rock climbing is as much a mental challenge as a physical one. Climbers face a dangerous paradox: fatigue clouds judgment just as muscles begin to fail, yet traditional safety systems can't detect these invisible tipping points. This research bridges that gap by developing a real-time monitoring system that simultaneously tracks cognitive stress through electroencephalography (EEG) and muscular fatigue via electromyography (EMG). The system employs machine learning algorithms to analyse physiological signals during climbing activities. EEG data identifies patterns associated with cognitive overload and fear responses, while EMG signals detect characteristic changes in forearm muscles that precede grip failure. Experimental validation demonstrated the system's ability to predict muscular fatigue 8.3 seconds (±1.2s) before failure occurs, with EEG-based stress detection achieving 87% accuracy compared to laboratory benchmarks. Unlike conventional post-climb assessments, this integrated approach provides immediate feedback when climbers approach critical physiological thresholds. The technical implementation addresses key challenges of biosignal acquisition in dynamic environments, including motion artifact reduction and real-time processing constraints. Beyond safety applications, the system generates objective metrics for performance analysis, offering insights into the relationship between cognitive states and physical endurance during climbing. This work establishes a framework for multimodal physiological monitoring in extreme sports, with potential applications extending to other domains where real-time assessment of cognitive and physical fatigue could enhance safety and performance. The findings highlight both the feasibility and remaining challenges of implementing biosignal-based safety systems in high-mobility environments.