Accurate estimation of bond strength between concrete and deformed reinforcing bars is essential for the stability of reinforced concrete structures, especially in critical regions subjected to heavy loads and environmental stresses. Despite intensive experimental studies revealing the complexity of factors influencing bond strength, existing predictive models, often reliant on artificial neural networks, have limitations in accuracy due to constrained datasets and inadequate representation of real-world stress fields. In response, this study pioneers a novel hybrid metaheuristic-optimized neural network model to swiftly and precisely predict bond strength under tensile load. Utilizing a comprehensive dataset comprising 558 valid experimental outcomes, seven metaheuristic algorithms are employed to optimize the ANN architecture. These metaheuristic algorithms include the Weighted Mean of Vectors, Grey Wolf Optimizer, Energy Valley Optimizer, Circle Search Algorithm, Artificial Ecosystem-Augmented Optimization, War Strategy Optimization, and Brown-Bear Optimization Algorithm. Results demonstrate that the developed hybrid models, particularly the artificial neural networks optimized by the Weighted Mean of Vectors algorithm, exhibit superior predictive performance. This model also demonstrated the lowest MISCALIBRATION value, followed by Circle Search Algorithm and Energy Valley Optimizer, indicating a high level of reliability. Moreover, comparison with common analytical and empirical formulations revealed significant performance improvements of the proposed model, achieving a 25% reduction in MSE during the testing phase. Additionally, the Shapley Additive explanations and Sobol sensitivity analysis framework was used to interpret the proposed predictive model, highlighting key predictors such as cross-sectional area, development length or splice, reinforcing bar diameter, and concrete compressive strength.