Video streaming platforms have been using the H.264/AVC standard for a long time, even though it was released almost 20 years ago and much more efficient codecs are currently available. The AOMedia Video 1 (AV1) format is an alternative with significant coding efficiency gains in comparison to H.264/AVC, besides being a royalty-free format. However, migrating legacy content from older to newer formats is a costly task, which requires long processing times. This work presents a solution for accelerating the H.264-to-AV1 transcoder based on machine learning. Sixteen decision tree models trained with data gathered during the H.264/AVC decoding and the AV1 encoding processes are proposed and implemented in the libaom reference software, leading to a complexity reduction of 14.3% at the cost of coding efficiency losses of 2.9% on average. To the best of the authors' knowledge, this is the first H.264-to-AV1 transcoding acceleration solution published in the literature.