Carbon Fiber Reinforced Polymer (CFRP) composites are widely used in reinforced concrete beams (RCBs) due to their high strength-to-weight ratio, corrosion resistance, and ease of application. However, accurately predicting the failure mode characteristics in these beams remains a significant challenge. This study presents a comprehensive framework for analyzing and predicting the failure modes of CFRP-strengthened RCBs using Deep Residual Neural Networks (DRNNs), finite element modeling (FEM), and Density-Based Sensitivity Analysis (DBSA). The proposed framework integrates experimental data and FEM simulations, leveraging DRNNs to model complex, nonlinear relationships between input parameters and failure mechanisms. DRNNs' architecture—comprising convolutional layers, residual building units, and batch normalization—facilitates accurate prediction of debonding loads, displacements, and failure modes. Residual shortcuts (i.e., connections), unlike other neural network architectures, allowed to bypass a few layers in the deep network architecture, circumventing the regular training with high accuracy problems. The DRNNs were instrumental in reducing reliance on large-scale physical experiments by generating synthetic data points required for DBSA convergence, making this framework practical for real-world applications. The study highlights the efficiency of DRNNs in reducing experimental requirements, achieving a high prediction accuracy of 90% and an AUC of 0.87. Validation of FEM with experimental results confirmed the framework's reliability in replicating real-world structural behavior. DBSA identified CFRP thickness and concrete density as critical factors influencing debonding load and displacement, while dry fiber density predominantly affects failure modes. Also, elastic properties of CFRP and concrete are essential for influencing debonding patterns.