Semiconductors with desirable electronic band structure and optical absorption are sought for solar cells, electronic devices, infrared sensors and quantum computing. In this work, we develop AI-based frameworks for the on-demand prediction of the phase stability, band gap, optical absorption spectra, photovoltaic figures of merit, dielectric constant, defect formation energies, and impurity energy levels in two broad classes of semiconductors, namely (a) halide perovskites, and (b) group IV, III-V and II-VI semiconductors. This framework is powered by high-throughput density functional theory (DFT) computations, unique encoding of the atom-composition-structure information, and rigorous training of advanced neural network-based predictive and optimization models. Multi-fidelity learning is applied to bridge the gap between (high quantities of) low accuracy calculations and (lower quantities of) accurate, expensive computations and experimental measurements. AI-based recommendations are synergistically coupled with targeted synthesis and characterization, leading to successful validation and discovery of novel compositions for improved performance in solar cells.