Diabetes mellitus is one of the most prevalent diseases that is growing rapidly around the world. Early intervention of blood glucose level not only helps to improve the management of diabetes mellitus but also reduces the cost of treatment. The aim of this review article is to provide an updated report on state of art technologies for minimally invasive, invasive and non-invasive glucose monitoring devices and sensors which are commercially available and provides a review of recent advancements and future prospects in this field. This article also explores the use of machine learning and deep learning algorithms for predicting the risk of diabetes mellitus based on blood glucose data. It emphasizes the importance of data quality in improving the accuracy of predictive models. It highlights the challenges faced in glucose monitoring and provides possible solutions. It concludes by emphasizing the potential of non-invasive wearable devices and artificial intelligence models in enhancing diabetes management and improving patient outcomes. Overall, this article provides a comprehensive overview of the methodology, techniques, algorithms, and available market devices for blood glucose monitoring, highlighting the importance of early diagnosis and accurate prediction in diabetes management.