Multi-view learning systems face significant reliability challenges in real-world applications due to sensor corruption, noise, and intermittent missing views.Current fusion strategies lack dynamic adaptation capabilities, compromising performance in safety-critical domains such as autonomous driving and medical diagnosis.We propose DAVE (Dynamic Adaptive View learning with Epistemic uncertainty), a robust multi-view framework that integrates uncertainty-aware stochastic encoders with a novel Uncertainty-Guided Adaptive Fusion (UGAF) module. Our approach dynamically weights view contributions based on real-time reliability estimates and incorporates robust training through stochastic view dropout and adversarial augmentation.Extensive evaluations across four diverse benchmarks demonstrate that DAVE achieves an average accuracy improvement of 8.6% under degraded conditions and reduces uncertainty miscalibration by 32% compared to state-of-the-art methods. The framework maintains robust performance even with 50% missing views and 40% sensor noise, establishing new standards for reliable multi-view learning.DAVE establishes a new paradigm for 1trustworthy multi-sensor systems by integrating principled uncertainty quantification with dynamic fusion. These advances enable reliable deployment in safety-critical applications where conventional multi-view approaches fail under real-world uncertainties.