Abstract: The production of fiber-reinforced concrete presents a complex reaction system, posing significant challenges in determining appropriate material proportions to achieve targeted mechanical properties. To address this issue, this study proposes novel Artificial Neural Network (ANN) models for predicting the compressive and flexural strengths of fiber-reinforced concrete using a backpropagation feed-forward algorithm. A wide range of concrete mix designs was prepared and tested using cylindrical samples for compressive strength and beam samples for flexural strength. Polypropylene fibers were incorporated into the mixes, and all specimens were cured for 28 days in a water-saturated lime solution. The results demonstrated that the ANN models produced strength predictions that closely aligned with experimental data, yielding high correlation values of 99.46% and 98.57% for compressive and flexural strengths, respectively. The best-fit models exhibited mean squared errors of 0.0024 (compressive) and 0.44 (flexural). Furthermore, parametric analysis indicated that the proposed models effectively captured the constitutive relationships among the concrete components and successfully represented the dominant mechanical behavior of the tested specimens.