Biocatalysis is an effective approach for producing chiral drug intermediates that are often difficult to synthesize using traditional chemical methods. A time-efficient strategy is required to accelerate the directed evolution process to achieve the desired enzyme function. In this research, we evaluated machine learning-assisted directed evolution as a potential approach for enzyme engineering, using a moderately diastereoselective ketoreductase library as a model system. Machine learning-assisted directed evolution and traditional directed evolution methods were compared for reducing (±)-tetrabenazine to dihydrotetrabenazine via kinetic resolution facilitated by BsSDR10, a short-chain dehydrogenase/reductase from Bacillus subtilis. Both methods successfully identified variants with significantly improved diastereoselectivity for each isomer of dihydrotetrabenazine. Furthermore, the preparation of (2S,3S,11bS)-dihydrotetrabenazine has been successfully scaled up, with an isolated yield of 40.7% and a diastereoselectivity of 91.3%.