In this research, a hybrid model based on machine learning capabilities by integrating the Random Forest Classifier with the Support Vector Classifier is presented. By employing this machine learning approach, we want to improve the precision and dependability of ethanol quality prediction, which will help optimize ethanol production processes and guarantee the delivery of high-quality ethanol products. To maximize machine learning capabilities, we first hybrid merge a Random Forest Classifier and a Support Vector Classifier. We will demonstrate and examine the variations between the Random Forest Classifier and Support Vector Classifier in terms of ethanol quality prediction. Support Vector Classifier, which has produced amazing outcomes, As it succeeds in a variety of NLP tasks, we are attempting to take advantage of its potent sequence modeling capacity for in-depth ethanol attribute analysis. After training on 80% of the dataset volume, the suggested ML classifier model is tested on 20% of the dataset. The results of two classifiers are divided into two groups for SPSS analysis, with each group having 10 outcome values under various functional activities before counting 20. The parameters CI and alpha in SPSS analysis are set to 0.03, and a G power of 0.95 is employed. The experimental analysis is conducted, and the accuracy gain of the two classifiers is compared using a Python compiler. A few of the compounds and acids in the collection have alcohol predictions. With an accuracy of 88.05%, the chosen Random Forest Classifier correctly predicted Ethanol, while the Support Vector Classifier achieved an accuracy of 90.87%.