Techniques for machine learning based coded size estimation in rate control of video encoding are described. An encoder in accordance with various embodiments uses one or more machine learning approaches to facilitate the rate control of video encoding. When training one or more neural network models, the relationship between the coded size and unit characteristics (e.g., picture pixels and/or picture features) is learned from past encoding. The encoder then uses the trained model(s) to estimate the coded size through model inference with improved accuracy. In some embodiments, the trained model(s) are integrated into the encoder for direct model inference. The direct model inference reduces the overhead of referencing application programming interfaces (APIs) provided by a separate machine learning platform, thus making the rate control methods and systems described herein useful in real time video encoding.