Abstract: Various embodiments of the present disclosure disclose a machine learning training approach for intelligently training a plurality of machine learning models associated with a multitask environment. The techniques include jointly training the plurality of machine learning models based on task similarities by generating a similarity matrix corresponding to a plurality machine learning models, generating a sharing loss value for the at least two machine learning models, generating, using a loss function and a training dataset, a prediction loss value for a particular machine learning model of the at least two machine learning models, generating an aggregated loss value for the particular machine learning model based on the similarity matrix, the sharing loss value, and the prediction loss value, and updating the particular machine learning model based on the aggregated loss value for the particular machine learning model.