The production of high-quality wine is strongly impacted by the quality of grape clusters, especially their sugar level, which is a key factor in deciding the best time to harvest. Destructive sample techniques that are labor-intensive, timeconsuming, and not scalable are traditionally used to evaluate grape maturity. This thesis addresses these limitations by investigating the non-destructive prediction of grape sugar concentration utilizing machine learning algorithms, such as regressions or neural networks, in conjunction with modern imaging technologies, with a primary focus on hyperspectral imaging (HSI). The study starts with a thorough overview of the principles of viticulture, covering grape biology, development phases, and the importance of sugar buildup during maturation.