Machine learning methods are integrated into the pipelined first level (L1) track trigger of the upgraded flavor physics experiment Belle II at KEK in Tsukuba, Japan. The novel triggering techniques cope with the severe background from events outside the small collision region provided by the new SuperKEKB asymmetric-energy electron-positron collider. Using the precise drift-time information of the central drift chamber which provides axial and stereo wire layers, a neural network L1 trigger estimates the 3D track parameters of tracks, based on input from the axial wire planes provided by a 2D track finder. An extension of this 2D Hough track finder to a 3D finder is proposed, where the single hit representations in the Hough plane are trained using Monte Carlo. This 3D finder improves the track finding efficiency by including the stereo sense wires as input. The estimated polar track angle allows a specialization of the subsequent neural networks to sectors in the polar angle.