The prediction of excitation band edge wavelength (EBEW) and peak emission wavelength (PEW) for Eu²⁺-activated phosphors is intricate in practice, although a theoretical interpretation has been well established. A data-driven approach could be of great help for EBEW and PEW prediction. We collected 91 Eu²⁺-activated phosphors, the host structures of which exhibit a single activator site and the EBEW and PEW of which are available at the critical activator concentration. We extracted 29 descriptors (input features) that implicate the elemental and structural traits of phosphor hosts, and set up an integrated machine-learning (ML) platform consisting of 18 ML algorithms that allowed prediction of the EBEW and PEW as well as the DFT-calculated band gap (E_g). The acquired dataset involving 91 phosphors was insufficient for the 29-input-feature problem and the real-world data collected from the literature have a so-called dirty nature due to inaccurate, unstandardized experiments. Despite an unavoidable paucity of data and the dirty-data problems of real-world data-based ML implementation, we obtained acceptable holdout dataset test results for PEW predications such as R² > 0.6, MSE < 0.02, and test_R²/training_R² > 0.77 for four ML algorithms. The EBEW and E_g predictions returned slightly better test results than these PEW examples.