Despite the recent surge of using various machine learning techniques for predicting the engineering performance of concrete, most of the efforts focus on predicting single properties. However, the proper design and use of concrete in real construction require additional considerations to many other properties, such as fluidity, air-content, and constructability. In this study, we trained a deep learning neural net based on a dataset of industrial concrete, which consists of more than 10,000 samples from the production. In particular, we adopt the cutting-edge machine learning techniques to train the model to predict multiple properties of a given concrete mix design. Importantly, the results suggest that our multi-target model exhibits a higher holistic accuracy as compared to its single-target oriented counterpart. In this sense, the multi-target machine learning prediction has a strong potential promote the multi-dimensional performance optimization of concrete mix design based on the actual needs of the construction.