Background: Effective management of glycemic levels is important tominimalizing complications associated with diabetes.The traditional methods like self-monitoring, regular clinical assessments and standardized treatment plans. That way frequently encounters issues such as patient non-compliance, excessive data and restricted clinical resources.The start of continuous glucose monitoring (CGM), insulin delivery systemsand wearable technology have generated substantial volumes of real-time data, underscoring the necessity for intelligent systems capable of efficiently processing and interpreting this information.Artificial intelligence (AI) presents promising solutions to improve diabetes management through personalized, adaptive and predictive strategies. Methods:This article consolidates recent research that utilized AI techniquesincluding machine learning (ML), deep learning (DL), reinforcement learning (RL) and natural language processing (NLP)in significant areas of glycemic management. The examination concentrated on applications like glucose forecasting, insulin dosing algorithms, closed-loop systems and digital coaching platforms. The studies were assessed according to the type of algorithm, sources of data input, validation techniques and clinical efficacy. A particular focus was given to model precision, interpretability, and compatibility with current diabetes technologies. Findings: Artificial Intelligence (AI) has shown notableimprovements in the management of diabetes. Machine Learning (ML) models have successfully forecasted hypoglycemia and categorized patient risk with using Continuous Glucose Monitoring (CGM) and electronic health record (EHR) data. Deep Learning (DL) frameworks like convolutional and recurrent neural networks, have attained high precision in predicting short-term glucose levels.