Rice serves as a staple food for more than half of the global population, making its authenticity and quality vital to consumer trust and food security. With numerous rice varieties available in the market, accurately identifying and classifying these varieties is crucial for maintaining supply chain integrity. Traditional methods of rice variety classification often rely on manual inspection and laboratory testing, which are time-consuming, resource-intensive, and prone to errors.This research explores the potential of advanced computer vision techniques and machine learning algorithms to automate and enhance rice variety classification. By training models on extensive datasets containing rice images and genetic information, the proposed system aims to autonomously and accurately identify rice varieties. The integration of machine learning enables the extraction of subtle visual and genetic features that are difficult to discern through traditional methods, ensuring precise and reliable classification.The adoption of this AI-driven approach has significant implications for the rice supply chain. It not only ensures consumers receive the quality and variety of rice they expect but also supports stakeholders in maintaining transparency and traceability. This work demonstrates how cutting-edge technologies can revolutionize rice classification processes, contributing to greater efficiency, authenticity, and trust across the global food supply chain.