In the field of sugar production, guaranteeing consistent quality is vital for both consumers and industries. The goal of this research is to establish models, based on machine learning, capable of classifying the produced sugar quality into 3 distinct classes viz. High, Low, and Medium. There are several parameters associated with the complex production process es of sugar Mill. The developed models will include the potential of machine learning techniques to speedily and precisely classify the sugar quality. To accomplish this, a secondary dataset based on numerous runs of sugar production has been used here to train and test two machine learning based models using the Decision Tree, and K-Means clustering techniques in MATLAB. The classification accuracy of both models has also been Investigated, The successful creation of machine learning based models for sugar quality classification within the sugar mill has substantial implications. By automation of classification process, impending defects or inconsistencies in sugar quality can be swiftly identified, enabling timely interventions for oprlmizing production process. This research provides noteworthy potential for overall sugar quality improvement, improving customer satisfaction, and reinforcing the competitivenes s of sugar industries in the market.