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Optimal walking trajectories are essential for bipedal walking. Recent developments enabled the use of motion planer based on nonlinear optimization. They are however, not directly applicable in real-time tasks due to a high computation time. Therefore, a task space consisting of the stride-length and a step time is used to precompute corresponding trajectory parameters in a certain range regarding a cost function. The resulting trajectories define an optimal joint space motion. The mapping from the task space to the joint space is generalized with multiple machine learning methods. A parametrization of every method was determined to represent the underlying model of the data as good as possible, without over-fitting. Finally, the performance regarding the accuracy and runtime and the evaluation of the cost value and constraint violation in the walking tasks is discussed.