Machine Learning (ML) and Deep Learning (DL) have become increasingly popular in the field of materials science for building property prediction models owing to their ability to efficiently extract and understand data-driven relationships between materials composition, structure, and properties. In general, materials property prediction are regression problems with a vector-based input material representation. While fully connected layers have been widely used in deep neural networks to predict materials properties, simply adding more and more layers to create a deep model often degrades their performance due to the vanishing gradient problem, thereby limiting usage. In this paper, we study and propose architectural principles for building deep regression neural networks comprising fully connected layers with numerical vectors that bypass manual feature engineering. We introduce a novel deep regression neural network with branched residual learning, BRNet, consisting of branching of layers to maximize variation of features learned from the input or previous layer and places skip connections after each layer to minimize the information loss due to vanishing gradient. We perform BRNet model training for inorganic material properties using numerical vectors representing the elemental fractions of the compositions of the respective materials and compare its performance against other traditional ML and DL techniques, including ElemNet and IRNet. Using multiple datasets (such as OQMD, MP, JARVIS) for training and testing, we show that BRNet models are significantly more accurate than the state-of-the-art ML methods and DL models for all data sizes by using only raw elemental fractions as input. We also show that BRNet’s branched residual learning requires fewer parameters and leads to better convergence during the training phase than other neural networks, thus resulting in faster model training.