High-throughput first-principles property calculations are often constrained by costly post-processing and dense Brillouin-zone sampling, impeding the creation of large, internally consistent materials-property datasets and limiting AI-driven discovery workflows. Pseudo-atomic-orbital (PAO) Hamiltonians provide an exact tight-binding representation of first-principles electronic structure that enables the calculation of a wide range of electronic, optical, topological, and transport properties at negligible cost, thereby supporting scalable generation of training-quality data and AI-ready data infrastructures. In this work, we present PAOFLOW 3.0 – an open-source Python suite that automates the construction and analysis of PAO Hamiltonians from plane-wave density functional theory calculations performed with either Quantum ESPRESSO or VASP. The resulting Hamiltonians enable efficient electronic structure interpolation, Fermi surface analysis, optical and dielectric response, transport coefficients, Berry phase and topological quantities, quantum transport, and other materials properties. Compared with previous releases, PAOFLOW 3.0 substantially extends the scope of the package through the introduction of internal projections enabling support for VASP calculations, self-consistent Hubbard U and V corrections obtained using ACBN0 and eACBN0 methods, generation of environment-dependent Slater-Koster tight-binding models, Landauer–BÃŒttiker quantum transport, and calculation of quantum oscillations using the integrated PySKEAF module. The theoretical foundations and the software architecture are presented together with representative calculations illustrating the current capabilities of the package.