This article presents a deep tensor neural network (DTNN) approach for quantum-chemical insights into molecular systems. The DTNN combines concepts from many-body Hamiltonians with deep tensor networks, enabling accurate predictions of molecular properties with uniform accuracy of 1 kcal mol⁻¹ across chemical space. The model provides spatial and chemical resolution of quantum-mechanical observables, revealing insights into molecular structures and properties. It is applied to predict atomic energies, local chemical potentials, isomer energies, and molecules with unique electronic structures. The DTNN is trained on large datasets of molecular structures and properties, including the GDB-7 and GDB-9 databases, and demonstrates high accuracy in predicting molecular energies and other properties. The model is also used to analyze the stability of aromatic rings and to predict the energy distribution of molecules in molecular dynamics simulations. The DTNN's ability to learn atomic-level representations and provide insights into chemical systems is highlighted, as well as its potential for future applications in chemical discovery and materials science. The model is scalable with molecular size and efficient, making it a promising tool for understanding complex quantum-chemical systems. The study demonstrates the potential of machine learning to reveal insights into quantum-chemical systems, with applications in drug discovery, materials science, and chemical engineering.This article presents a deep tensor neural network (DTNN) approach for quantum-chemical insights into molecular systems. The DTNN combines concepts from many-body Hamiltonians with deep tensor networks, enabling accurate predictions of molecular properties with uniform accuracy of 1 kcal mol⁻¹ across chemical space. The model provides spatial and chemical resolution of quantum-mechanical observables, revealing insights into molecular structures and properties. It is applied to predict atomic energies, local chemical potentials, isomer energies, and molecules with unique electronic structures. The DTNN is trained on large datasets of molecular structures and properties, including the GDB-7 and GDB-9 databases, and demonstrates high accuracy in predicting molecular energies and other properties. The model is also used to analyze the stability of aromatic rings and to predict the energy distribution of molecules in molecular dynamics simulations. The DTNN's ability to learn atomic-level representations and provide insights into chemical systems is highlighted, as well as its potential for future applications in chemical discovery and materials science. The model is scalable with molecular size and efficient, making it a promising tool for understanding complex quantum-chemical systems. The study demonstrates the potential of machine learning to reveal insights into quantum-chemical systems, with applications in drug discovery, materials science, and chemical engineering.