Shape-based methods are used in the preliminary optimization of low-thrust trajectories to rapidly search large design spaces and provide initial guesses for higher fidelity methods. The optimization process benefits from the shape-based methods providing initial guesses as quickly and as optimal as possible. This work aims to improve the hodographic shaping method by implementing machine learning (ML) to optimize its free parameters. The addition of free parameters enables a more optimal trajectory, while the ML model reduces the computational effort required to optimize this trajectory. ML incorporated shaping models with 6 and 9 Degrees of Freedom (DoF) and models with different levels of mission generalization are built. The models are tested and compared to the shaping method without ML on different types of single transfers in a grid search and on more complex missions in a genetic algorithm. The ML 6-DoF models can consistently find trajectories that are more optimal compared to the 0-DoF models without ML, by up to 6 km/s for an Earth-Ceres transfer, and they can do so 50-100 times faster than the 6-DoF models without ML. The 9-DoF models produce inconsistent results and can only improve no-ML shaping in areas of low optimality. The ML models significantly accelerate the performance of the genetic algorithm when the relevant transfer bodies are included in the ML training data.