Machine learning (ML) has emerged as a pivotal tool in the design of light-emitting materials, significantly accelerating the discovery process by enabling precise predictions of material properties. Through the analysis of extensive datasets, ML facilitates material discovery and optimization, particularly in predicting emission characteristics and designing innovative compounds. When integrated with physics-based modeling approaches such as Density Functional Theory (DFT) and molecular dynamics (MD) simulations, ML further enhances the prediction and refinement of properties like emission wavelength, efficiency, and stability. Additionally, inverse design leverages computational methods alongside ML to develop novel material structures, fine-tune properties, and optimize performance. These advancements have notably improved applications in energy-efficient organic light-emitting diodes (OLEDs), LEDs, fiber-optic communication, and biophotonics. Integration with artificial  intelligence, nanotechnology, and bioinformatics expands their applications, including surgical precision and deep-tissue imaging, advancing the capabilities of biomedical technologies. Despite persistent challenges related to data quality, model interpretability, and computational costs, progress in ML algorithms and interdisciplinary collaboration continues to drive advancements in the biomedical field.