Traditional markets are characterized by fast-paced and diverse transactions, necessitating a reliable product monitoring system to enhance the efficiency of stock management and transactions. This study develops a monitoring system based on a loadcell sensor and a TCS230 color sensor to automatically classify product weight and type. The loadcell is used to measure product weight with high accuracy, while the TCS230 detects the color characteristics of products to distinguish between different types of commodities, such as various varieties of chili peppers. The development process includes sensor calibration, dataset collection, and the training and evaluation of a classification model. Experimental results show that the classification accuracy exceeds 90%, demonstrating the effectiveness of combining weight and color data for market product recognition.