This study investigates the application of Random Forest models to predict bending after impact flexural failure modes in carbon and glass fiber reinforced polymer hybrid composites. Samples were tested under varying conditions, including low (−70 °C) and room temperatures, and impact energies of 15, 30, and 45 J. Key parameters such as dent depth, delamination length, and facesheet modulus were measured, and different flexural failure modes were documented. The study utilized RandomForestRegressor to predict dent depth, delamination length, and facesheet modulus, achieving high R2 values and low Mean Squared Errors. RandomForestClassifier was employed to predict bending after impact failure modes, achieving an excellent accuracy of 0.9772 and an extremely low Mean Squared Error of 0.0064. The models accurately predicted failure modes, with only minor mispredictions explained through additional data analysis. Feature importance analysis revealed that bending type and temperature were the most significant factors influencing failure modes, followed by dent depth and material type. The application of Random Forest models demonstrated a reliable and effective approach for predicting post-impact flexural failure modes in hybrid composites, offering valuable insights for optimizing material design and impact testing processes.