Use of Technology in agriculture and marketing of agricultural products is essential in sustainable and quality production. Some of the usage areas of these technologies are quality control and classification of grains. To optimize the rice production and processing industry, ensuring best product quality and meeting consumer demands effectively, different varieties of rice grain need to be classified accurately and consistently. Manual classification of rice is laborious, time consuming, inconsistent, and inefficient. Our main objective is developing an Artificial Intelligence (AI) based automated model that can analyze and classify rice grains with high accuracy, allowing for higher throughput and increased productivity. In such, we proposed a Machine Learning (ML) based approach to classify five classes of rice varieties. Investigated the results of five classifiers namely, Logistic Regression (LR), K-Nearest Neighbors (KNN), Naive Bayes (NB), Decision Tree (DT) and Random Forest (RF). The RF classifier has given 99.40% accuracy in classifying the five varieties of rice grains.