Ultraviolet cured-in-place-pipe (UV-CIPP) materials are commonly used in trenchless pipeline rehabilitation. Their bending strength is a crucial indicator to evaluate the curing quality. Studies show that this indicator is affected by multiple factors, including the curing time, UV lamp curing power, curing distance, and material thickness. Laboratory experiments have limitations in analyzing the effect of multiple factors on the bending strength of UV-CIPP materials and quantitatively predicting the optimum curing parameters. Aiming at resolving these shortcomings, resolve machine learning techniques were applied to predict the bending strength. In this regard, the surface curing reaction temperature monitoring data and three-point bending data of 30 groups of UV-CIPP material under the influence of different curing parameters were used as a dataset to predict the bending strength of UV-CIPP material. The results show that the influence degree of each factor on the bending strength of the UV-CIPP material, from high to low, is as follows: UV lamp power (−0.439), the temperature at the illuminated side (−0.392), curing time (−0.323), the temperature at the back side (−0.233), curing distance (0.143) and material thickness (−0.140). The best penalty parameter c (44.435) and width g (0.072) of the kernel function in the support vector machine (SVM) model were obtained using the genetic algorithm (GA) optimization, and the results were compared with the grey wolf optimizer (GWO) and particle swarm optimization (PSO). The performed analyses revealed that the developed GA-SVM model exhibits the best prediction results compared to other machine learning algorithms. The optimum bending strength of the UV-CIPP material used in this test is 294.77 MPa, which corresponds to the curing time, UV lamp power, curing distance, material thickness, light side temperature, and back side temperature of 7.59 min, 157.33 mW/cm2, 189.99 mm, 4.38 mm, 79.49 °C, and 76.59 °C, respectively.