Abstract Air‐plasma‐sprayed thermal barrier coating analysis faces three key challenges: informational complexity from overlapping defect morphologies, semantic ambiguity from gradient boundaries, and data scarcity from asymmetric feature distribution. Conventional segmentation approaches struggle particularly with distinguishing unmelted from melt‐solidified regions. This research proposes TBC‐HybridNet, a confidence‐guided feature‐fusion architecture combining the specialized UnmeltedSegNET with generic deep convolutional neural networks through hierarchical fusion. UnmeltedSegNET employs multiscale modules to extract contextual information, integrating large receptive fields for structural integrity with small receptive fields for edge preservation, outperforming human annotators with 97.8% accuracy in boundary detection. The framework implements a confidence‐guided fusion strategy that dynamically adjusts model weights, addressing data imbalance while maintaining sensitivity to rare defects without computationally intensive retraining. The system achieves 97.9% accuracy for unmelted regions, 91.8% overall accuracy, and an 88.3% F1 score for cracks. It enables real‐time quantification of critical quality metrics, including unmelted volume fraction and crack density. With 96.6% crack continuity detection and 71.1% unmelted boundary fidelity, these capabilities establish precise correlations between spray processes and microstructure, improving coating durability prediction in aerospace applications and directly impacting turbine engine performance and service life.