With the increasing use of advanced materials in resilient structures, glass fiber-reinforced polymer (GFRP) bars have emerged as a lightweight and corrosion-resistant alternative to traditional steel reinforcement. However, the bond behavior between GFRP bars and high-strength concrete is not yet fully understood, making accurate prediction a challenge. This study aims to develop a data-driven model for predicting the bond strength between GFRP bars and high-strength concrete. A database of 162 experimental specimens was compiled from reputable sources, including features such as bar diameter, embedment length, concrete cover, compressive strength, elastic modulus, and tensile strength. Three machine learning algorithms including Decision Tree, Random Forest, and K-Nearest Neighbors, were used, with grid search applied for hyperparameter optimization. The Decision Tree model outperformed the others, achieving an R² of 0.93, MAE of 1.53 MPa, and RMSE of 1.92 MPa. The K-Nearest Neighbors model also improved significantly, with R² increasing from 0.69 to 0.89, though still lagging behind the Decision Tree. The Random Forest showed moderate improvement but lower accuracy. Feature importance analysis revealed that embedment length was the most influential parameter, contributing nearly half of the model’s predictive power. This study demonstrates that machine learning models—particularly the Decision Tree—are effective, accurate, and interpretable tools for predicting the bond behavior of GFRP bars in high-strength concrete.