Abstract: The development of traditional luminescent materials, such as cadmium-based quantum dots and lead halide perovskites, are intrinsically limited by their reliance on toxic heavy metals (e.g., Cd and Pb), which raises severe environmental and health risks throughout their lifecycles. Therefore, the transition toward eco-friendly alternatives—including cadmium-free quantum dots, lead-free halide perovskites, and rare-earth-doped phosphors—has become a pivotal research imperative. Currently, the design and optimization of such materials often rely on inefficient trial-and-error experimental paradigms, which often fail to overcome critical bottlenecks in luminous efficiency, environmental stability, and interfacial compatibility. This review systematically outlines the current landscape and technical challenges of eco-friendly luminescent materials. We highlight how computational techniques, particularly density functional theory (DFT), allow the accurate prediction of optoelectronic properties in core-shell structures and the elucidation of defect-induced non-radiative recombination mechanisms, thus facilitating rational material design and property optimization. In addition to theoretical calculations, data-driven technologies further accelerate material screening by leveraging standardized databases and machine learning models, having already yielded high-stability phosphors and high-efficiency narrowband emitters. Finally, we provide an outlook on the synergy between computational and data-driven approaches to overcome existing research and development barriers. Future efforts must focus on deepening the integration of these technologies to advance the practical deployment of eco-friendly luminescent materials in display and lighting applications, thereby driving the sustainable transformation of the optoelectronics industry.

Key words: friendly luminescent materials, density functional theory, data-driven technology, machine learning, perovskite materials, review