The application of the Tsetlin Machine to predict HbA1c levels in diabetes patients treated with homeopathy focuses on improving accuracy in personalized diabetes management. The primary aim is to use the Tsetlin Machine's pattern recognition capabilities to identify key factors, such as treatment duration, blood glucose trends, insulin sensitivity, lifestyle factors, and comorbid conditions, that influence HbA1c levels. This approach offers a reliable, non-invasive model to assist clinicians in optimizing homeopathic treatments for better glycemic control. By analyzing historical patient data, the machine detects correlations between treatments and HbA1c outcomes, allowing for more effective diabetes management. Data from two instances in the HbA1c_Prediction_Dataset was taken as a sample. In the first instance, five features were analyzed for five patients, with blood glucose trends ranging from 0.2196 to 0.9944 and insulin sensitivity from 0.0021 to 0.7692. The second instance examined fasting blood sugar (0.1995 to 0.7831) and BMI (0.2200 to 0.9511) among other factors. This method supports enhancing predictive accuracy for patients undergoing homeopathic treatment for diabetes. © 2025 Elsevier B.V., All rights reserved.