Rice, a staple crop of paramount global importance, is instrumental in ensuring food security. As the world’s population continues to grow, the demand for increased rice production becomes increasingly vital. Accurate and efficient classification of rice types is indispensable in agriculture. Differentiating various rice varieties is crucial for effective crop management, quality control, and addressing consumer preferences. Historically, the classification process has relied on manual assessments, making it labor-intensive and susceptible to human error. In recent years, the integration of machine learning techniques, particularly ensemble methods like Decision Trees and Random Forest, has revolutionized rice type prediction. This contribution of this work is primary focus is on developing a model capable of automatically categorizing rice grains into their respective varieties based on meticulously extracted morphological features. Additionally, feature selection techniques are applied to enhance the model’s efficiency, mitigate over fitting, and improve prediction accuracy. This research is of paramount significance. Automating rice type classification not only reduces human error but also delivers swift and consistent results, benefiting both farmers and consumers. This advancement holds the potential to enhance crop management efficiency, optimize resource allocation, and elevate product quality control.