Low levels of vitamin D with type 2 diabetes mellitus (T2DM) are two prevalent health concerns that often coexist, impacting patient outcomes and healthcare costs. Accurate prediction of vitamin D deficiency in T2DM patients is crucial for optimizing treatment strategies and improving overall wellbeing. This review paper explores incorporating methods from artificial intelligence (AI) to enhance the accuracy of predicting vitamin D deficiency in T2DM patients. The paper highlights the potential of AI, including machine learning and deep learning algorithms, in harnessing diverse data sources such as patient records, genetic data, and laboratory results to predict vitamin D deficiency. We discuss the advantages of AI-driven prediction models in overcoming limitations of traditional methods, offering opportunities for early detection and personalized intervention. Nevertheless, we also address challenges surrounding data quality, privacy, and interpretability inherent in AI applications. This review underscores the importance of further research and collaboration in this evolving field, paving the way for improved patient care and outcomes.