Rare earth-doped metal oxide nanoparticles (RE-MO NPs) are notable for their enhanced electronic, optical, magnetic, and catalytic properties, influenced by the incorporation of elements like Ce, Eu, La, and Gd into oxides such as TiO2, ZnO, and Fe2O3. These dopants help create defect states and optimize band structures, which boosts performance in various fields including photocatalysis and nanomedicine. However, the rational design of RE-MO NPs is challenged by complex interactions between composition, structure, and synthesis. This review discusses the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in addressing these challenges through predictive modeling and optimization techniques. It explores various AI/ML methods, including supervised and unsupervised learning, deep learning, and generative models, for tasks like property prediction and materials discovery. The paper also highlights future advancements such as autonomous labs and explainable AI to enhance the understanding of structure–property relationships. Overall, it emphasizes the significant impact of AI/ML in accelerating the discovery and intelligent design of rare earth nanomaterials for advanced applications.